License Plate Recognition From Still Images and Video Sequences: A Survey
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paper_title: An efficient method of license plate location

paper_content:

License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method.
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paper_title: Real-Time License Plate Recognition on an Embedded DSP-Platform

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In this paper we present a full-featured license plate detection and recognition system. The system is implemented on an embedded DSP platform and processes a video stream in real-time. It consists of a detection and a character recognition module. The detector is based on the AdaBoost approach presented by Viola and Jones. Detected license plates are segmented into individual characters by using a region-based approach. Character classification is performed with support vector classification. In order to speed up the detection process on the embedded device, a Kalman tracker is integrated into the system. The search area of the detector is limited to locations where the next location of a license plate is predicted. Furthermore, classification results of subsequent frames are combined to improve the class accuracy. The major advantages of our system are its real-time capability and that it does not require any additional sensor input (e.g. from infrared sensors) except a video stream. We evaluate our system on a large number of vehicles and license plates using bad quality video and show that the low resolution can be partly compensated by combining classification results of subsequent frames.
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paper_title: A Cascade Framework for a Real-Time Statistical Plate Recognition System

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This paper describes a plate recognition system that can process images rapidly at high accuracy rates. This system is designed to meet the requirements of performance, computational speed, and adaptation for vehicle surveillance applications, such as stolen car detection systems. These requirements are satisfied by adopting a cascade framework, utilizing plate characteristics, and developing fast one-pass algorithms. Our system is composed of three main cascading modules for plate detection, character segmentation, and postprocessing. Each module is further decomposed into several cascading procedures, which are composed of successively more complex rejecters. The first module rapidly rejects a majority of nonplate regions by using low computational gradient features and a one-pass scanning algorithm followed by heavy computational statistical rejecters. The second module rejects a majority of noncharacter regions in a similar manner. A peak-valley analysis algorithm is proposed to rapidly detect all promising candidates of character regions. The third module eliminates the plate characters that do not satisfy the plate specifications. In our experiments, the system can recognize plates over 38 frames per second with a resolution of 640 times 480 pixels on a 3-GHz Intel Pentium 4 personal computer
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paper_title: An efficient method of license plate location

paper_content:

License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method.
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paper_title: An Efficient Features - Based License Plate Localization Method

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This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.
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paper_title: A Macao license plate recognition system

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A license plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring and security systems. This paper proposes an experimental license plate recognition system for Macao-style license plates. The system uses morphological operations and a projection searching algorithm for the extraction of license plates. The recognition result is obtained from a character recognition based on template matching. The proposed work in this paper is the first attempt towards Macao-style license plates.
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paper_title: A robust license-plate extraction method under complex image conditions

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A robust approach for extracting car license plate from images with complex background and relatively poor quality is presented. The approach focuses on dealing with images taken under weak lighting condition. The proposed method is divided into two steps: 1) searching candidate areas from the input image using gradient information, and 2) determining the plate area among the candidates and adjusting the boundary of the area by introducing a plate template. A set of experiments has been performed to prove the robustness and accuracy of the approach. For many images collected from a large underground parking place the result shows that 90% of them are correctly segmented.
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paper_title: Detection and recognition of license plate characters with different appearances

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This paper proposes an approach to developing an automatic license plate recognition system. Car images are taken from various positions outdoors. Because of the variations of angles from the camera to the car, license plates have various locations and rotation angles in an image. In the license plate detection phase, the magnitude of the vertical gradients is used to detect candidate license plate regions. These candidate regions are then evaluated based on three geometrical features: the ratio of width and height, the size and the orientation. The last feature is defined by the major axis. In the character recognition phase, we must detect character features that are non-sensitive to the rotation variations. The various rotated character images of a specific character can be normalized to the same orientation based on the major axis of the character image. The crossing counts and peripheral background area of an input character image are selected as the features for rotation-free character recognition. Experimental results show that the license plates detection method can correctly extract all license plates from 102 car images taken outdoors and the rotation-free character recognition method can achieve an accuracy rate of 98.6%.
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paper_title: FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators

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Modern FPGA platforms provide the hardware and software infrastructure for building a bus-based system on chip (SoC) that meet the applications requirements. The designer can customize the hardware by selecting from a large number of pre-defined peripherals and fixed IP functions and by providing new hardware, typically expressed using RTL. Hardware accelerators that provide application-specific extensions to the computational capabilities of a system is an efficient mechanism to enhance the performance and reduce the power dissipation. What is missing is an integrated approach to identify the computationally critical parts of the application and to create accelerators starting from a high level representation with a minimal design effort. In this paper, we present an automation methodology and a tool that generates accelerators. We apply the methodology on an FPGA-based license plate recognition (LPR) system used in law enforcement. The accelerators process streaming data and support a programming model which can naturally express a large number of embedded applications resulting in efficient hardware implementations. We show that we can achieve an overall LPR application speed up from 1.2/spl times/ to 2.6/spl times/, thus enabling real-time functionality under realistic road scenes.
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paper_title: A Morphology Based Approach for Car License Plate Extraction

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Locating the car license plate in an image or video frame of a car is an important step in car license plate recognition/identification applications. This problem poses many challenges like location of license plate from images taken in poor illumination and bad weather condition; plates that are partly obscured by dirt and images that have low contrast. This paper presents a new morphology based method for license plate extraction from car images. The algorithm uses morphological operations on the preprocessed, edge images of the vehicles. Characteristic features such as license plate width and height, character height and spacing are considered for defining structural elements for morphological operations. Connected component analysis is used to select the band containing license plate from the candidate segmented. The experimental results with a reasonably large set of car images are very encouraging.
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paper_title: License Plate Extraction in Low Resolution Video

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Extensive study has been conducted in the detection of license plate for the applications in intelligent transportation system (ITS). However, these results are all based on images acquired at a resolution of 640 times 480. In this paper, a new method is proposed to extract license plate from the surveillance video which is shot at lower resolution (320 times 240) as well as degraded by video compression. Morphological operations of bottom-hat and morphology gradient are utilized to detect the LP candidates, and effective schemes are applied to select the correct one. The average rates of correct extraction and false alarms are 96.62% and 1.77%, respectively, based on the experiments using more than four hours of video. The experimental results demonstrate the effectiveness and robustness of the proposed method
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paper_title: Vehicle license plate recognition in marketing application

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The paper presents an artificial vision system able to analyze the image of a car given by a camera, to locate the registration plate and to recognize its registration number. It describes practical problems encountered in implementing this application and the proposed solutions. The system has been designed using a modular approach. Sub-modules can be upgraded and or substituted independently, thus making the system potentially suitable in a large variety of vision applications. Performances of the system, have been evaluated in real situation.
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paper_title: Vehicle license plate location based on histogramming and mathematical morphology

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In a vehicle license plate identification system, plate region detection is the key step before the final recognition. This paper presents a license plate detection algorithm from complex background based on histogramming and mathematical morphology. The proposed algorithm consists of two main modules: license plate region's rough detection and license plate exactly location. The former characterized by vertical gradients detection extracts candidate regions from an input image, while the latter conceptualized in terms of mathematical morphology aims to locate the license plate fast and accurately. Experiments have been conducted for this algorithm, and 360 images taken from various scenes were employed, including diverse angles, different lightening conditions and the dynamic conditions. The algorithm can quickly and correctly detect the region of license plate and the license plate detecting rate of success is 97.78%.
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paper_title: New Methods for Automatic Reading of VLP's (Vehicle License Plates)

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ABSTRACT In a former work, we built a VLP (Vehicle License Plates)reader using computer vision. To test other methods, wedecided to implement a whole new system from scratch. Inthis communication, we summarize the methods that wehave tested and the results we have obtained. Platelocation is based on mathematical morphology andcharacter recognition is implemented using Hausdorffdistance. Results are comparable to those obtained withother methods. KEY WORDS Computer Vision, Morphologic Analysis, TextRecognition, Hausdorff Distance. 1. Introduction This communication describes the development of anautomatic VLP recognizer. Such a system has manypractical applications: security and accounting systems inparking facilities, traffic monitoring, stolen car detectionand other law enforcement applications...To develop this system we cleared our minds and decidedto use methods that were new for us. Our purpose was totest these methods and to compare them to other methodswe had used previously in the same problem [1]. Our nextproject is to merge both systems to get an improved one.The structure of this work is as follows:a) Recognition Algorithms: this part is the core of thepaper and explains the different computer visionmethods. We used mathematical morphology forlocating the plate. We also used image projectionsfor segmenting the caracters (this is a widely usedmethod of O.C.R. technology, but we had neverused it for plate images). Finally, we tested theHausdorff Distance for character recognition(Haussdorf Distance is a well behaved method forcomparing bilevel images).b) Practical Results: here, we see the global resultsachieved by our system. We also compare thissystem to our previous system.c) Future Lines.d) Conclusions.There are other publications that describe VLPrecognizing. For example [2] uses syntactic patternrecognition, [3] also uses morphology for location andneural networks for recognition (they do not publish theirresults), in [4] they use a bunch of heuristical methods(they get good partial results but they do not publish theglobal ones).
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paper_title: A high performance license plate recognition system based on the web technique

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The license plate recognition (LPR) system is one kind of Intelligent transportation systems (ITS) and is of considerable interest because of its potential applications to such areas as highway electronic toll collection, red-light violation enforcement, secure-access control at parking lots and so on. The paper introduces the system framework and work flow of our LPR system which works based on the web technique. Some problems of the technical emphases and realization are also addressed. Capabilities under practical conditions show the effectiveness of the system.
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paper_title: A license plate locating algorithm based on multiple gauss filters and morphology mathematics

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In this paper, we propose a new automatic located algorithm of license plate based on multiple Gauss filters and morphology mathematics, there are two steps in our algorithm, first, to locate the license plate coarsely from a car image based on multiple Gauss filters, it contains three steps, firstly, to compute the vertical difference of the standardized image and then to get the horizontal projective curve of the difference image, secondly, to filter the curve by a window gauss filter and then to transform the smooth horizontal projective curve by another Gauss function, thirdly, to locate the license plate coarsely by seeking the vales of the transformed curve. Second step, to extract the license plate accurately based on morphology mathematics from the coarse license plate, it contain three steps, firstly, to compute the vertical difference of the coarse license plate and then to process the difference image by morphology operation, secondly, to get the vertical projective curve of the difference image and then to smooth the curve by window gauss filter, thirdly, the accurate position of the license plate can be got by seeking the wave of maximal area of the curve. Finally, the accurate license plate can be got after these steps, the experimental results have proved that this is an effective, robust and fast method to locate the license plate from a car image captured from different backgrounds.
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paper_title: Vehicle License Plate Segmentation in Natural Images

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A robust method for plate segmentation in a License Plate Recognition (LPR) system is presented, designed to work in a wide range of acquisition conditions, including unrestricted scene environments, light, perspective and camera-to-car distance. Although this novel text-region segmentation technique has been applied to a very specific problem, it is extensible to more general contexts, like difficult text segmentation tasks dealing with natural images. Extensive experimentation has been performed in order to estimate the best parameters for the task at hand, and the results obtained are presented.
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paper_title: Extraction and recognition of license plates of motorcycles and vehicles on highways

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A recognition system is proposed to extract and recognize license plates of motorcycles and vehicles on highways. In the first stage, a block-difference method is used to detect moving objects. According to the variance and the similarity of the M/spl times/N blocks defined on two diagonal lines, the blocks are categorized as three kinds: low-contrast, stationary and moving blocks. In the second stage, a screening method based on the projection of edge magnitudes is used to find two peaks in the projection histograms to bound license plates. The scanning lines with low counts can be removed. In the third stage, character images are segmented and recognized. In our experiments, we tested 180 pairs of images. The block-difference method has a 98% success rate and can remove 88% of pixels from an image on average. The screening method has a 94.4% success rate and the character recognition method has a 95.7% precision rate.
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paper_title: A novel license plate localization method based on textural feature analysis

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Automatic license plate localization in real-time environment is a complex task, due to the variety of sizes and colors as well as to the complexity of the background. In this paper we address this problem by proposing a novel localization scheme. The proposed approach is designed to work in a wide range of acquisition conditions, including unrestricted scene environments, camera-to-car distance, etc. A four-stage search process is proposed in this paper: pre-processing, approximate region searching, region classification and skew correction using Radon transform. Experiments have been preformed to prove the robustness and accuracy of the approach. The experiment results show that almost 96.1 % of input images are correctly localized on the average
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paper_title: Number plate reading using computer vision

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The main tasks of a number plate recognition system are the location of the number plate area in the image, the segmentation of the characters and their identification. These tasks are strongly inter-related, mainly because the way to check if the number plate has been correctly located is based on the result of the character identification process (it should correspond to a predefined syntax). Algorithmic improvements to previous versions of the system, based on the results of intensive testing, are described in this paper. For the location of the number plate area in the image, a new line-based method has been developed. The method, instead of looking for character like shapes in the image, takes advantage of the "signature" of the number plate area in a horizontal cross-section of the image. The method used for the segmentation of the characters is derived from a technique first proposed by Lu (1995). The identification of the characters uses the OCR engine developed by Barroso et al. (1995), based on the critical points method.
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paper_title: A NEURAL NETWORK BASED ARTIFICIAL VISION SYSTEM FOR LICENSE PLATE RECOGNITION

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This paper describes a neural network based vision system that is able to analyze the image of a car captured by a camera, locate the registration plate and recognize the registration number of the car. The paper also describes the different practical problems encountered in implementing this application and the solutions used to solve them.
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paper_title: Application of pattern recognition for Farsi license plate recognition

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A heart valve holder is provided for implanting a prosthetic heart valve in either an inflow (mitral or tricuspid) or an outflow (aortic or pulmonary) implantation. The valve holder may be repeatedly sterilized for multiple implantations. The valve holder includes holding members to securely hold the valve, a valve-sizing member to allow the holder to hold valves of various sizes, an over rotation stop to prevent the holder from impinging upon the valve leaflets, alignment members to align the holding members for receiving a valve, and means to rotate the valve without damaging the valve leaflets.
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paper_title: Optical recognition of motor vehicle license plates

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A system for the recognition of car license plates is presented. The aim of the system is to read automatically the Italian license number of a car passing through a tollgate. A CCTV camera and a frame grabber card are used to acquire a rear-view image of the vehicle. The recognition process consists of three main phases. First, a segmentation phase locates the license plate within the image. Then, a procedure based upon feature projection estimates some image parameters needed to normalize the license plate characters. Finally, the character recognizer extracts some feature points and uses template matching operators to get a robust solution under multiple acquisition conditions. A test has been done on more than three thousand real images acquired under different weather and illumination conditions, thus obtaining a recognition rate close to 91%. >
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paper_title: Vector quantization for license-plate location and image coding

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License-plate location in sensor images plays an important role in vehicle identification for automated transport systems (ATS). This paper presents a novel method based on vector quantization (VQ) to process vehicle images. The proposed method makes it possible to perform superior picture compression for archival purposes and to support effective location at the same time. As compared with classical approaches, VQ encoding can give some hints about the contents of image regions; such additional information can be exploited to boost location performance. The VQ system can be trained by way of examples; this gives the advantages of adaptiveness and on-field tuning. The approach has been tested in a real industrial application and included satisfactorily in a complete ATS for vehicle identification.
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paper_title: A template-guided approach to vehicle surveillance and access control

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Video-based surveillance techniques have extended their scope to the field of undervehicle inspection. However, their role is currently restricted to the visualization of the inspection results, while the evaluation of their outcome depends solely on the judgement of the operator. This paper presents an intelligent template-guided approach, which addresses the task of computer-assisted undervehicle inspection and access control. It combines a license plate identification stage which authenticates incoming vehicles and a template-guided undervehicle inspection stage which alerts the operator in case of questionable content alterations.
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paper_title: Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces

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A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net.
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paper_title: CONDENSATION—Conditional Density Propagation for Visual Tracking

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The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.
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paper_title: Automatic car license plate extraction using modified generalized symmetry transform and image warping

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This paper proposes a method which evaluates symmetry of plate corners and extracts car license plates captured from the arbitrary directions. The generalized symmetry transform (GST) produces continuous features of symmetry between two points by combining locality constraint and reflectional symmetry. Also, we propose scan line based GST to improve time complexity of the GST significantly due to selective attention to particular distortions in case of a large searching window. The corners of a plate are detected from our modified scan line based GST with selective directions using the fact that a corner is formed by two neighboring straight lines acquired in a symmetry map of GST. However, a rotated or perspectively distorted car license plate image is very difficult for segmentation of interior characters. Image normalization by image warping is adopted to make such segmentation of license plate and later identification much easier. We also adopt the verifier which evaluates a candidate license plate to enhance extraction rate. Our experiments show that the proposed method for extracting perspectively distorted license plates is fairly reliable.
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paper_title: A novel license plate localization method based on textural feature analysis

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Automatic license plate localization in real-time environment is a complex task, due to the variety of sizes and colors as well as to the complexity of the background. In this paper we address this problem by proposing a novel localization scheme. The proposed approach is designed to work in a wide range of acquisition conditions, including unrestricted scene environments, camera-to-car distance, etc. A four-stage search process is proposed in this paper: pre-processing, approximate region searching, region classification and skew correction using Radon transform. Experiments have been preformed to prove the robustness and accuracy of the approach. The experiment results show that almost 96.1 % of input images are correctly localized on the average
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paper_title: An efficient implementation of the Hough transform for detecting vehicle license plates using DSP'S

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The paper addresses the general problem of the detection of vehicle license plates from road scenes, for the purpose of vehicle tracking. It describes an algorithm for detecting a license plate from a road scene acquired by a CCD camera, using image processing techniques, where the authors have used the Hough transform (HT) for line detection (the shape of the license plates is defined by lines) because it fits well into this application as it facilitates the selection of lines from specific areas and specific orientations within the image. This is followed by a brief description of the image processing system on which this algorithm has been implemented. Lastly, the results and conclusions of the application of the license plate detection algorithm to a real vehicle are discussed.
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paper_title: Car license plate recognition based on the combination of principal components analysis and radial basis function networks

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This paper proposes a car license plate recognition system. First, an approach based on histogram analysis method together with Hough transform is presented for segmentation. It is robust to car plate inclination within 15 degree. Subsequently, in the recognition phase, we use principle components analysis to extract the feature of input images and then feed to neural networks with radial basis function networks is used for symbols recognition. Experimental results show that the PCA method proposed reduces the dimension of image effectively and the recognition performance using RBF networks with 98% is better than back-propagation (BP) with 93%.
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paper_title: License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization

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This paper presents a novel algorithm for license plate detection and license plate character segmentation problems by using the Gabor transform in detection and local vector quantization in segmentation. As of our knowledge this is the first application of Gabor filters to license plate segmentation problem. Even though much of the research efforts are devoted to the edge or global thresholding-based approaches, it is more practical and efficient to analyze the image in certain directions and scales utilizing the Gabor transform instead of error-prone edge detection or thresholding. Gabor filter response only gives a rough estimate of the plate boundary. Then binary split tree is used for vector quantization in order to extract the exact boundary and segment the plate region into disjoint characters which become ready for the optical character recognition.
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paper_title: Hough Transform from the Radon Transform

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An appropriate special case of a transform developed by J. Radon in 1917 is shown to have the major properties of the Hough transform which is useful for finding line segments in digital pictures. Such an observation may be useful in further efforts to generalize the Hough transform. Techniques for applying the Radon transform to lines and pixels are developed through examples, and the appropriate generalization to arbitrary curves is discussed.
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paper_title: Multiple license plate detection for complex background

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This paper presents a wavelet transform based method for extracting license plates from cluttered images. The proposed system consists of three major stages. First, a wavelet transform based method is used for extracting important contrast features as guides to search for desired license plates. Then, finding a reference line in HL subimage plays an important role to locate the desired license plate region roughly. According to the reference line we can decrease the searching region of license plate and speed up the execution time. The last stage of the method is to locate the license plate accurately by license plate adjustment. More importantly, the proposed detection method can locate multiple plates with different orientations in one image. Since the feature extracted is robust to complex backgrounds, the proposed method works well in extracting differently illuminated and oriented license plates. The average accuracy of detection is 92.4%.
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paper_title: Rectangle detection based on a windowed Hough transform

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The problem of detecting rectangular structures in images arises in many applications, from building extraction in aerial images to particle detection in cryo-electron microscopy. This paper proposes a new technique for rectangle detection using a windowed Hough transform. Every pixel of the image is scanned, and a sliding window is used to compute the Hough transform of small regions of the image. Peaks of the Hough image (which correspond to line segments) are then extracted, and a rectangle is detected when four extracted peaks satisfy certain geometric conditions. Experimental results indicate that the proposed technique produced promising results for both synthetic and natural images.
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paper_title: Automatic license plate recognition system based on color image processing

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A License plate recognition (LPR) system can be divided into the following steps: preprocessing, plate region extraction, plate region thresholding, character segmentation, character recognition and post-processing. For step 2, a combination of color and shape information of plate is used and a satisfactory extraction result is achieved. For step 3, first channel is selected, then threshold is computed and finally the region is thresholded. For step 4, the character is segmented along vertical, horizontal direction and some tentative optimizations are applied. For step 5, minimum Euclidean distance based template matching is used. And for those confusing characters such as '8' & 'B' and '0' & 'D', a special processing is necessary. And for the final step, validity is checked by machine and manual. The experiment performed by program based on aforementioned algorithms indicates that our LPR system based on color image processing is quite quick and accurate.
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paper_title: Car license plate recognition with neural networks and fuzzy logic

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A car license plate recognition system (CLPR-system) has been developed to identify vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural and fuzzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate, of 0.02% at a recognition rate of 98.51%. These results are also compared with other published CLPR-systems.
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paper_title: Automatic license number extraction and its parallel implementation

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This paper presents a segmentation technique based on multiple threshold values determined by fuzzy logic and information entropy principle to extract alphanumeric characters from general scene images, and its parallel implementation in a cluster of personal computers in a local area network. In this approach, the scene images are segmented into various regions based on fuzzy-entropy thresholding method which allows each local threshold to be detected and used for segmentation. A coarse searching technique is then implemented to locate potential character regions, followed by a rule-based character detecting technique to accurately extract characters. This searching step is executed in parallel at each computer for each selected threshold so as to collect all possible character-candidate regions. These results are then combined and x-y projection/profile is further applied to search for any missing characters. The multithreshold segmentation technique offers a simple, robust system that is capable of extracting alphanumeric characters from various scene images under nonuniform illuminating condition and for a variety of car number plates whereas the execution speed of the system is increased with parallel computing and dynamic load balancing technique.
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paper_title: Mean shift for accurate license plate localization

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This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region represents a real license plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to above three features to classify license plate regions and non-license plate regions. Experimental results show that the proposed algorithm produces high robustness and accuracy.
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paper_title: Mean shift, mode seeking, and clustering

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Mean shift, a simple interactive procedure that shifts each data point to the average of data points in its neighborhood is generalized and analyzed in the paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on the surface constructed with a "shadow" kernal. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis if treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications in clustering and Hough transform are demonstrated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization. >
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paper_title: Gaussian Weighted Histogram Intersection for License Plate Classification

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The conventional histogram intersection (HI) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. Since this algorithm is strictly based on the matching between bins of identical colors, the final matching rate can be easily affected by color variation caused by various environment changes. In this paper, a Gaussian weighted histogram intersection (GWHI) algorithm is proposed to facilitate the histogram matching via taking into account matching of both identical and similar colors. The weight is determined by the distance between two colors. The algorithm is applied to license plate classification. Experimental results show that the proposed algorithm produces a much lower intra-class distance and a much higher inter-class distance than previous HI algorithms for tested images which are captured under various illumination conditions.
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paper_title: An adaptive approach to vehicle license plate localization

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It is well known that locating vehicle license plate is a very difficult problem in the field of image segmentation and ITS (intelligent transportation system). And unfortunately most of the methods already presented don't work robustly and usually impose some special restrictions on the input image condition, such as illumination, noise, etc. In this paper we proposed an accurate and robust localization algorithm by employing an adaptive thresholding and search strategy. The algorithm is based on the spatial frequency characteristic that the grayscale in the license plate region usually varies more frequently than in the background due to the existence of characters. In this paper, an adaptive threshold is firstly reached through an iterative selection-verification process. The gray image is then binarized by this threshold, and the algorithm localizes the license plate in the binarized image combining some prior knowledge of the license plate. The final accurate license plate segmentation is achieved after an accurate edge detection process based on the color information of the license plate. The robustness and performance of the algorithm are demonstrated by the convincing segmentation results even when the images are rather complex, noisy and in poor illumination.
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paper_title: Merged-color histogram for color image retrieval

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Conventional histogram-based image retrieval algorithms usually find only intersecting areas of the color-component distributions of images, and thus work well in matching images with exact colors instead of similar colors, especially for computer generated pictures. This could be greatly affected by overall variations, such as intensity changes. A novel merged-color histogram (MCH) method for color image retrieval is proposed to facilitate matching between similar colors by means of color quantization and palette merging. Color quantization compacts the color information and matches each color instead of color components, and matching of similar colors is accomplished using palette merging. Experimental results show that the proposed MCH method is about 11-32% more precise in the first 20 retrievals for the same image query, and is able to recall 14%-23% more relevant images in the first 100 retrievals, as compared to the conventional RGB-based histogram method.
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paper_title: Mean shift: A robust approach toward feature space analysis

paper_content:

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
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paper_title: The fuzzy logic approach to the car number plate locating problem

paper_content:

Fuzzy logic is applied to the problem of locating the number plate in digital images of cars. The intention of the paper is to show the capability of fuzzy logic in this field. Our aim was to locate the number plate by use of intuitive rules and experience. By extending or changing the rules the system can easily be adapted to locate other patterns. The results obtained by the system are very good and reliable.
---
paper_title: The Application of a Convolution Neural Network on Face and License Plate Detection

paper_content:

In this paper, two detectors, one for face and the other for license plates, are proposed, both based on a modified convolutional neural network(CNN) verifier. In our proposed verifier, a single feature map and a fully connected MLP were trained by examples to classify the possible candidates. Pyramid-based localization techniques were applied to fuse the candidates and to identify the regions of faces or license plates. In addition, geometrical rules filtered out false alarms in license plate detection. Some experimental results are given to show the effectiveness of the approach. Keywords: Face detection, license plate detection, convolution neural network, feature map.
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paper_title: Locating car license plate under various illumination conditions using genetic algorithm

paper_content:

In this paper genetic algorithm is used for the license plate locating problem. To overcome the degradation of license plates in vehicle images under various illumination conditions, a windowed local area is adaptively binarized and the feature vector is obtained from a bank of filter. After that, the real integer coded genetic algorithm is proposed to search the whole image for the most promising area. Experimental results show that the local area processing combined with GA is quite robust with images of various illumination conditions compared with other techniques.
---
paper_title: Color Texture-Based Object Detection: An Application to License Plate Localization

paper_content:

This paper presents a novel color texture-based method for object detection in images. To demonstrate our technique, a vehicle license plate (LP) localization system is developed. A support vector machine (SVM) is used to analyze the color textural properties of LPs. No external feature extraction module is used, rather the color values of the raw pixels that make up the color textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, LP regions are identified by applying a continuously adaptive meanshift algorithm (CAMShift) to the results of the color texture analysis. The combination of CAMShift and SVMs produces not only robust and but also efficient LP detection as time-consuming color texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be analyzed.
---
paper_title: Learning-based approach for license plate recognition

paper_content:

Presents a learning-based approach for the construction of a license-plate recognition system. The system consists of three modules. They are, respectively, the car detection module, the license-plate segmentation module and the recognition module. The car detection module detects a car in a given image sequence obtained from a camera with a simple color-based approach. The segmentation module extracts the license plate in the detected car image using neural networks as filters for analyzing the color and texture properties of the license plate. The recognition module then reads the characters on the detected license plate with a support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from toll-gates, parking lots, etc., and has shown the following performances on average: car detection rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%.
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paper_title: Research on number-plate recognition based on neural networks

paper_content:

The paper presents a technique of number-plate recognition based on neural networks. Each number-plate in a color image can be correctly located by analyzing number-plate colors which are classified by neural networks. An integrated neural network and template matching method is used to recognize characters of a number-plate. Experimental results show that the correct rate of number-plate locations is close to 100%, and the number-plate locating time is less than 3 seconds using the proposed method. Moreover, recognition rate of characters is improved due to the known number-plate types and novel method of character recognition. It is also observed that our algorithm is not sensitive to variations of weather, illumination and vehicle speed. Another advantage of the proposed method is that the size of number-plate is not required to be known a prior.
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paper_title: A practical license plate recognition system for real-time environments

paper_content:

A computer vision system to recognize license plates of vehicles in real-time environments is presented in this study. The images of moving vehicles are taken with a digital camera and analyzed in real-time. An artificial neural network (ANN) system is used to locate the area and position of the license plate. The system has the following stages: (i) Image acquisition and determination of the location of the vehicle license plate (VLP), (ii) segmentation of the VLP into separate characters using image processing techniques, and (iii) recognition of each symbol in VLP using a feedforward artificial neural network (ANN) and assembly of the characters. Performance results are presented at the end.
---
paper_title: Real time face and object tracking as a component of a perceptual user interface

paper_content:

As a step towards a perceptual user interface, an object tracking algorithm is developed and demonstrated tracking human faces. Computer vision algorithms that are intended to form part of a perceptual user interface must be fast and efficient. They must be able to track in real time and yet not absorb a major share of computational resources. An efficient, new algorithm is described here based on the mean shift algorithm. The mean shift algorithm robustly finds the mode (peak) of probability distributions. We first describe histogram based methods of producing object probability distributions. In our case, we want to track the mode of an object's probability distribution within a video scene. Since the probability distribution of the object can change and move dynamically in time, the mean shift algorithm is modified to deal with dynamically changing probability distributions. The modified algorithm is called the Continuously Adaptive Mean Shift (CAMSHIFT) algorithm. CAMSHIFT is then used as an interface for games and graphics.
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paper_title: Car license plate recognition with neural networks and fuzzy logic

paper_content:

A car license plate recognition system (CLPR-system) has been developed to identify vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural and fuzzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate, of 0.02% at a recognition rate of 98.51%. These results are also compared with other published CLPR-systems.
---
paper_title: Phoneme recognition using time-delay neural networks

paper_content:

The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: (1) using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation; and (2) the time-delay arrangement enables the network to discover acoustic-phonetic features and the temporal relationships between them independently of position in time and therefore not blurred by temporal shifts in the input. As a recognition task, the speaker-dependent recognition of the phonemes B, D, and G in varying phonetic contexts was chosen. For comparison, several discrete hidden Markov models (HMM) were trained to perform the same task. Performance evaluation over 1946 testing tokens from three speakers showed that the TDNN achieves a recognition rate of 98.5% correct while the rate obtained by the best of the HMMs was only 93.7%. >
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paper_title: Research on Vehicle License Plate Location Based on Neural Networks

paper_content:

There are some usual methods in vehicle license plate location, such as segmentation in grey-level image, color image edge extraction and neural networks filters etc. All these methods are proved not quite satisfactory in various conditions, or are influenced by some factors. In this paper, we present to classify colors of pixels by using improved neural networks, which include 27 nodes of input layer, 30 nodes of hidden layer and 6 nodes of output layer. Several candidate plate regions are extracted from the results of classification. Then a criterion including the features of areas, the ratios of width to height and vertical projection histogram is proposed to decide a real license plate region. Experimental results show that this method has a high locating rate, and adapts to various conditions.
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paper_title: PCNN models and applications.

paper_content:

The pulse coupled neural network (PCNN) models are described. The linking field modulation term is shown to be a universal feature of any biologically grounded dendritic model. Applications and implementations of PCNN's are reviewed. Application based variations and simplifications are summarized. The PCNN image decomposition (factoring) model is described in new detail.
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paper_title: Convolutional face finder: a neural architecture for fast and robust face detection

paper_content:

In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.
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paper_title: Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

paper_content:

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.
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paper_title: Object localization using color, texture and shape

paper_content:

Abstract We address the problem of localizing objects using color, texture and shape. Given a handrawn sketch for querying an object shape, and its color and texture, the proposed algorithm automatically searches the image database for objects which meet the query attributes. The database images do not need to be presegmented or annotated. The proposed algorithm operates in two stages. In the first stage, we use local texture and color features to find a small number of candidate images in the database, and identify regions in the candidate images which share similar texture and color as the query. To speed up the processing, the texture and color features are directly extracted from the Discrete Cosine Transform (DCT) compressed domain. In the second stage, we use a deformable template matching method to match the query shape to the image edges at the locations which possess the desired texture and color attributes. This algorithm is different from other content-based image retrieval algorithms in that: (i) no presegmentation of the database images is needed, and (ii) the color and texture features are directly extracted from the compressed images. Experimental results demonstrate performance of the algorithm and show that substantial computational savings can be achieved by utilizing multiple image cues.
---
paper_title: License Plate Detection Using Hereditary Threshold Determine Method

paper_content:

License plate recognition is very important in an automobile society. Also in it, since plate detection has big influence on subsequent number recognition, it is very important. However, it is very difficult to do it, because a background and a body color of cars are similar to that of the license plate. In this paper, we propose a new thresholds determination method in the various background by using the real-coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds)are obtained by RGA to estimate thresholds function by using the recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.
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paper_title: License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization

paper_content:

This paper presents a novel algorithm for license plate detection and license plate character segmentation problems by using the Gabor transform in detection and local vector quantization in segmentation. As of our knowledge this is the first application of Gabor filters to license plate segmentation problem. Even though much of the research efforts are devoted to the edge or global thresholding-based approaches, it is more practical and efficient to analyze the image in certain directions and scales utilizing the Gabor transform instead of error-prone edge detection or thresholding. Gabor filter response only gives a rough estimate of the plate boundary. Then binary split tree is used for vector quantization in order to extract the exact boundary and segment the plate region into disjoint characters which become ready for the optical character recognition.
---
paper_title: Neural nets for fuzzy systems

paper_content:

Abstract We show how to represent fuzzy expert systems and fuzzy controllers as neural nets and as fuzzy neural nets. We show that fuzzy neural nets produce a more compact representation of fuzzy systems.
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paper_title: License plate recognition using DTCNNs

paper_content:

Automatic license plate recognition requires a series of complex image processing steps. For practical use, the amount of data to be processed must be minimized at early stage. This paper shows that the computationally most intensive steps can be realized by discrete time cellular neural networks (DTCNNs). Moreover, high-level operations like 'finding the license plate in the image' and 'finding the characters on the plate' need only a small number of DTCNNs. Real-life tests show that the DTCNNs are capable of correctly identifying more than 85% out of all license plates while leaving only 0.5% of the original information to be inspected for actual recognition.
---
paper_title: A fuzzy neural network and its application to pattern recognition

paper_content:

Defines four types of fuzzy neurons and proposes the structure of a four-layer feedforward fuzzy neural network (FNN) and its associated learning algorithm. The proposed four-layer FNN performs well when used to recognize shifted and distorted training patterns. When an input pattern is provided, the network first fuzzifies this pattern and then computes the similarities of this pattern to all of the learned patterns. The network then reaches a conclusion by selecting the learned pattern with the highest similarity and gives a nonfuzzy output. The 26 English alphabets and the 10 Arabic numerals, each represented by 16/spl times/16 pixels, were used as original training patterns. In the simulation experiments, the original 36 exemplar patterns were shifted in eight directions by 1 pixel (6.25% to 8.84%) and 2 pixels (12.5% to 17.68%). After the FNN has been trained by the 36 exemplar patterns, the FNN can recall all of the learned patterns with 100% recognition rate. It can also recognize patterns shifted by 1 pixel in eight directions with 100% recognition rate and patterns shifted by 2 pixels in eight directions with an average recognition rate of 92.01%. After the FNN has been trained by the 36 exemplar patterns and 72 shifted patterns, it can recognize patterns shifted by 1 pixel with 100% recognition rate and patterns shifted by 2 pixels with an average recognition rate of 98.61%. The authors have also tested the FNN with 10 kinds of distorted patterns for each of the 36 exemplars. The FNN can recognize all of the distorted patterns with 100% recognition rate. The proposed FNN can also be adapted for applications in some other pattern recognition problems. >
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paper_title: Locating car license plates using neural networks

paper_content:

The authors present a method for locating car license plates using neural networks. Neural networks are used as filters for analysing small windows of an image and deciding whether each window contains a license plate. A post-processor combines these filtered images and gives the final location of the license plates. The method offers robustness when dealing with noisy images. Tests with car images travelling on the road and at the entrance of a car park showed extraction rates of 99 and 97.5%, respectively. These results suggest that the proposed method works well with real-world situations.
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paper_title: A Flexible Neural Analog Using Integrated Circuits

paper_content:

An electronic neural analog is described which contains variable absolute and relative refractory periods, two time constants, and separate control of the ``accommodation'' to sub-threshold voltage changes and the ``adaptation'' produced by the occurrence of output pulses (spikes). The extensive use of integrated-circuit operational amplifiers permits an accurate description of input-output relationships over a wide range of values for all parameters. This facilitates comparison of the results obtained both with neural data and mathematical or digitally simulated models of neural activity. The effect of white noise on interval histograms and their parameters is described and its effect when added to other inputs. Noise disrupts the phase-locked patterns produced by sinusoidal stimuli and the averaged response may become a smooth sinusoidal function in the presence of added noise. Adaptation may produce a phase lead to sinusoidal stimuli, while accommodation may produce a phase lag. Corresponding overshoots or undershoots are seen with square-wave inputs.
---
paper_title: A Cascade Framework for a Real-Time Statistical Plate Recognition System

paper_content:

This paper describes a plate recognition system that can process images rapidly at high accuracy rates. This system is designed to meet the requirements of performance, computational speed, and adaptation for vehicle surveillance applications, such as stolen car detection systems. These requirements are satisfied by adopting a cascade framework, utilizing plate characteristics, and developing fast one-pass algorithms. Our system is composed of three main cascading modules for plate detection, character segmentation, and postprocessing. Each module is further decomposed into several cascading procedures, which are composed of successively more complex rejecters. The first module rapidly rejects a majority of nonplate regions by using low computational gradient features and a one-pass scanning algorithm followed by heavy computational statistical rejecters. The second module rejects a majority of noncharacter regions in a similar manner. A peak-valley analysis algorithm is proposed to rapidly detect all promising candidates of character regions. The third module eliminates the plate characters that do not satisfy the plate specifications. In our experiments, the system can recognize plates over 38 frames per second with a resolution of 640 times 480 pixels on a 3-GHz Intel Pentium 4 personal computer
---
paper_title: Training support vector machines: an application to face detection

paper_content:

We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs., 1985) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.
---
paper_title: Learning-Based License Plate Detection Using Global and Local Features

paper_content:

This paper presents a license plate detection algor ithm using both global edge features and local Haar -like features. Global classifiers using global statistic al features are constructed through simple learning processes. After applying these global classifiers, most of li cense plate background regions are excluded from further training or testing. Then an AdaBoost learning proc edure is constructed to obtain local classifiers ba sed on selected local Haar-like features. By combining the global and local classifiers, we obtain a cascaded classifier consisting of 6 layers with only 160 features. Ther efore, our cascaded classifier is simple and effici ent. Our proposed cascaded classifier is invariant to the br ightness, colour, size and position of license plat es. Satisfactory detection results showing high detection rate with low false positive rate are demonstrated using a vi deo of moving vehicles. Unlike methods built on either fix ed image backgrounds or fixed cameras, our method does not use any inter-frame information and pre-knowledge. Therefore, our method is more flexible for real-tim e applications.
---
paper_title: Transformational DT-CNN Design from Morphological Specifications

paper_content:

Morphology provides the algebraic means to specify operations on images. Discrete-time cellular neural networks (DT-CNNs) mechanize the execution of operations on images. The paper first shows the equivalence between morphological functions and DT-CNNs. Then, the argument is extended to the synthesis of optimal DT-CNN structures from complex morphological expressions. It is shown that morphological specifications may be freely derived, to be subsequently transformed and adopted to the needs of a specific target terminology. This process of technology mapping can be automated along the well-trodden path in CAD for microelectronics.
---
paper_title: Extracting characters of license plates from video sequences

paper_content:

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle, given a sequence of perspective-distortion-corrected license plate images. Different from many existing single-frame approaches, our method simultaneously utilizes spatial and temporal information. We first model the extraction of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing a posteriori probability based on a given prior knowledge and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted and some of our experimental results are presented in the paper. It is shown that our approach provides better performance than other single frame methods.
---
paper_title: Vehicle License Plate Segmentation in Natural Images

paper_content:

A robust method for plate segmentation in a License Plate Recognition (LPR) system is presented, designed to work in a wide range of acquisition conditions, including unrestricted scene environments, light, perspective and camera-to-car distance. Although this novel text-region segmentation technique has been applied to a very specific problem, it is extensible to more general contexts, like difficult text segmentation tasks dealing with natural images. Extensive experimentation has been performed in order to estimate the best parameters for the task at hand, and the results obtained are presented.
---
paper_title: A practical license plate recognition system for real-time environments

paper_content:

A computer vision system to recognize license plates of vehicles in real-time environments is presented in this study. The images of moving vehicles are taken with a digital camera and analyzed in real-time. An artificial neural network (ANN) system is used to locate the area and position of the license plate. The system has the following stages: (i) Image acquisition and determination of the location of the vehicle license plate (VLP), (ii) segmentation of the VLP into separate characters using image processing techniques, and (iii) recognition of each symbol in VLP using a feedforward artificial neural network (ANN) and assembly of the characters. Performance results are presented at the end.
---
paper_title: An Efficient Features - Based License Plate Localization Method

paper_content:

This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.
---
paper_title: Car license plate recognition with neural networks and fuzzy logic

paper_content:

A car license plate recognition system (CLPR-system) has been developed to identify vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural and fuzzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate, of 0.02% at a recognition rate of 98.51%. These results are also compared with other published CLPR-systems.
---
paper_title: A Binocular License Plate Reader for High Precision Speed Measurement

paper_content:

The paper describes a vision processing system for traffic speed computation. Vehicle tracking is performed by using high-level features, that are clusters of license plate characters to achieve increased robustness of the system. By using a binocular arrangement of the Computer Vision system, the spatial and temporal range of image capture between consecutive views is extended, which leads to an improved accuracy in speed computation. Multiple views can be collected, from the same vehicle in transit, depending on the amount of speed. The effectiveness of the proposed solution is demonstrated through a geometric simulation model, where almost all operating conditions and constraints are fully exploited and tested. An error sensitivity analysis is carried out, to identify the most critical components of the system and the possible sources of errors in speed computation. This simulation approach is proved quite useful in this complex scenario, where it is commonly very difficult to collect true speed measures from the vehicles in transit. Beside the simulation analysis, a series of experimental results have been collected by a first prototype that is available since the end of year 2000.
---
paper_title: Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

paper_content:

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.
---
paper_title: A NEURAL NETWORK BASED ARTIFICIAL VISION SYSTEM FOR LICENSE PLATE RECOGNITION

paper_content:

This paper describes a neural network based vision system that is able to analyze the image of a car captured by a camera, locate the registration plate and recognize the registration number of the car. The paper also describes the different practical problems encountered in implementing this application and the solutions used to solve them.
---
paper_title: License plate recognition using DTCNNs

paper_content:

Automatic license plate recognition requires a series of complex image processing steps. For practical use, the amount of data to be processed must be minimized at early stage. This paper shows that the computationally most intensive steps can be realized by discrete time cellular neural networks (DTCNNs). Moreover, high-level operations like 'finding the license plate in the image' and 'finding the characters on the plate' need only a small number of DTCNNs. Real-life tests show that the DTCNNs are capable of correctly identifying more than 85% out of all license plates while leaving only 0.5% of the original information to be inspected for actual recognition.
---
paper_title: Robust License Plate Detection Using Covariance Descriptor in a Neural Network Framework

paper_content:

We present a license plate detection algorithm that employs a novel image descriptor. Instead of using conventional gradient filters and intensity histograms, we compute a covariance matrix of low-level pixel-wise features within a given image window. Unlike the existing approaches, this matrix effectively captures both statistical and spatial properties within the window. We normalize the covariance matrix using local variance scores and restructure the unique coefficients into a feature vector form. Then, we feed these coefficients into a multi-layer neural network. Since no explicit similarity or distance computation is required in this framework, we are able to keep the computational load of the detection process low. To further accelerate the covariance matrix extraction process, we adapt an integral image based data propagation technique. Our extensive analysis shows that the detection process is robust against noise, illumination distortions, and rotation. In addition, the presented method does not require careful fine tuning of the decision boundaries.
---
paper_title: A Cascade Framework for a Real-Time Statistical Plate Recognition System

paper_content:

This paper describes a plate recognition system that can process images rapidly at high accuracy rates. This system is designed to meet the requirements of performance, computational speed, and adaptation for vehicle surveillance applications, such as stolen car detection systems. These requirements are satisfied by adopting a cascade framework, utilizing plate characteristics, and developing fast one-pass algorithms. Our system is composed of three main cascading modules for plate detection, character segmentation, and postprocessing. Each module is further decomposed into several cascading procedures, which are composed of successively more complex rejecters. The first module rapidly rejects a majority of nonplate regions by using low computational gradient features and a one-pass scanning algorithm followed by heavy computational statistical rejecters. The second module rejects a majority of noncharacter regions in a similar manner. A peak-valley analysis algorithm is proposed to rapidly detect all promising candidates of character regions. The third module eliminates the plate characters that do not satisfy the plate specifications. In our experiments, the system can recognize plates over 38 frames per second with a resolution of 640 times 480 pixels on a 3-GHz Intel Pentium 4 personal computer
---
paper_title: Learning-Based License Plate Detection Using Global and Local Features

paper_content:

This paper presents a license plate detection algor ithm using both global edge features and local Haar -like features. Global classifiers using global statistic al features are constructed through simple learning processes. After applying these global classifiers, most of li cense plate background regions are excluded from further training or testing. Then an AdaBoost learning proc edure is constructed to obtain local classifiers ba sed on selected local Haar-like features. By combining the global and local classifiers, we obtain a cascaded classifier consisting of 6 layers with only 160 features. Ther efore, our cascaded classifier is simple and effici ent. Our proposed cascaded classifier is invariant to the br ightness, colour, size and position of license plat es. Satisfactory detection results showing high detection rate with low false positive rate are demonstrated using a vi deo of moving vehicles. Unlike methods built on either fix ed image backgrounds or fixed cameras, our method does not use any inter-frame information and pre-knowledge. Therefore, our method is more flexible for real-tim e applications.
---
paper_title: An adaptive approach to vehicle license plate localization

paper_content:

It is well known that locating vehicle license plate is a very difficult problem in the field of image segmentation and ITS (intelligent transportation system). And unfortunately most of the methods already presented don't work robustly and usually impose some special restrictions on the input image condition, such as illumination, noise, etc. In this paper we proposed an accurate and robust localization algorithm by employing an adaptive thresholding and search strategy. The algorithm is based on the spatial frequency characteristic that the grayscale in the license plate region usually varies more frequently than in the background due to the existence of characters. In this paper, an adaptive threshold is firstly reached through an iterative selection-verification process. The gray image is then binarized by this threshold, and the algorithm localizes the license plate in the binarized image combining some prior knowledge of the license plate. The final accurate license plate segmentation is achieved after an accurate edge detection process based on the color information of the license plate. The robustness and performance of the algorithm are demonstrated by the convincing segmentation results even when the images are rather complex, noisy and in poor illumination.
---
paper_title: Intelligent traffic management through MPEG-7 vehicle flow surveillance

paper_content:

Recent development in the aspects of low-level image processing for feature extraction, as well as in the standardization of description schemes for video description, provide means for the development of Intelligent Transportation Systems. ITS may support a wide range of abilities, including vehicle identification and reidentification, event detection and optimum resource management. This paper analyzes the structure operation principles and the structure of an integrated highway management system. Its operation is based on the combination of sensor networks, low-level image processing algorithms and high-level description schemes. Low-level algorithms perform the tasks of license plate recognition for vehicle identification, as well as feature extraction through change detection, for event detection purposes. High level schemes are based on the recently developed MPEG-7 Description Schemes and are utilized as an information processing framework, which formulates identification and event representation procedures.
---
paper_title: A high performance license plate recognition system

paper_content:

This paper presents a powerful automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates was 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.
---
paper_title: Robust license-plate recognition method for passing vehicles under outside environment

paper_content:

Novel methods to recognize license plates robustly are presented. A sensing system with a wide dynamic range has been developed to acquire fine images of vehicles under varied illumination conditions. The developed sensing system can expand the dynamic range of the image by combining a pair of images taken under different exposure conditions. In order to avert blurring of images against fast passing vehicles, a prism beam splitter installed a multilayered filter, and two charge-coupled devices are utilized to capture those images simultaneously. Furthermore, to extend the flexibility of camera placement, a recognition algorithm that can be applied to inclined plates has been developed. The performance of recognizing registration numbers on license plates has been investigated on real images of about 1000 vehicles captured under various illumination conditions. Recognition rates of over 99% (conventional plates) and over 97% (highly inclined plates) showed that the developed system is quite effective for license-plate recognition.
---
paper_title: Optical recognition of motor vehicle license plates

paper_content:

A system for the recognition of car license plates is presented. The aim of the system is to read automatically the Italian license number of a car passing through a tollgate. A CCTV camera and a frame grabber card are used to acquire a rear-view image of the vehicle. The recognition process consists of three main phases. First, a segmentation phase locates the license plate within the image. Then, a procedure based upon feature projection estimates some image parameters needed to normalize the license plate characters. Finally, the character recognizer extracts some feature points and uses template matching operators to get a robust solution under multiple acquisition conditions. A test has been done on more than three thousand real images acquired under different weather and illumination conditions, thus obtaining a recognition rate close to 91%. >
---
paper_title: A fast marching level set method for monotonically advancing fronts.

paper_content:

Abstract ::: A fast marching level set method is presented for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential equation for a propagating level set function and use techniques borrowed from hyperbolic conservation laws. Topological changes, corner and cusp development, and accurate determination of geometric properties such as curvature and normal direction are naturally obtained in this setting. This paper describes a particular case of such methods for interfaces whose speed depends only on local position. The technique works by coupling work on entropy conditions for interface motion, the theory of viscosity solutions for Hamilton-Jacobi equations, and fast adaptive narrow band level set methods. The technique is applicable to a variety of problems, including shape-from-shading problems, lithographic development calculations in microchip manufacturing, and arrival time problems in control theory.
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paper_title: Automatic license plate recognition system based on color image processing

paper_content:

A License plate recognition (LPR) system can be divided into the following steps: preprocessing, plate region extraction, plate region thresholding, character segmentation, character recognition and post-processing. For step 2, a combination of color and shape information of plate is used and a satisfactory extraction result is achieved. For step 3, first channel is selected, then threshold is computed and finally the region is thresholded. For step 4, the character is segmented along vertical, horizontal direction and some tentative optimizations are applied. For step 5, minimum Euclidean distance based template matching is used. And for those confusing characters such as '8' & 'B' and '0' & 'D', a special processing is necessary. And for the final step, validity is checked by machine and manual. The experiment performed by program based on aforementioned algorithms indicates that our LPR system based on color image processing is quite quick and accurate.
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paper_title: Learning-based approach for license plate recognition

paper_content:

Presents a learning-based approach for the construction of a license-plate recognition system. The system consists of three modules. They are, respectively, the car detection module, the license-plate segmentation module and the recognition module. The car detection module detects a car in a given image sequence obtained from a camera with a simple color-based approach. The segmentation module extracts the license plate in the detected car image using neural networks as filters for analyzing the color and texture properties of the license plate. The recognition module then reads the characters on the detected license plate with a support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from toll-gates, parking lots, etc., and has shown the following performances on average: car detection rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%.
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paper_title: A practical license plate recognition system for real-time environments

paper_content:

A computer vision system to recognize license plates of vehicles in real-time environments is presented in this study. The images of moving vehicles are taken with a digital camera and analyzed in real-time. An artificial neural network (ANN) system is used to locate the area and position of the license plate. The system has the following stages: (i) Image acquisition and determination of the location of the vehicle license plate (VLP), (ii) segmentation of the VLP into separate characters using image processing techniques, and (iii) recognition of each symbol in VLP using a feedforward artificial neural network (ANN) and assembly of the characters. Performance results are presented at the end.
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paper_title: An Efficient Features - Based License Plate Localization Method

paper_content:

This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.
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paper_title: Car license plate recognition with neural networks and fuzzy logic

paper_content:

A car license plate recognition system (CLPR-system) has been developed to identify vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural and fuzzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate, of 0.02% at a recognition rate of 98.51%. These results are also compared with other published CLPR-systems.
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paper_title: A Macao license plate recognition system

paper_content:

A license plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring and security systems. This paper proposes an experimental license plate recognition system for Macao-style license plates. The system uses morphological operations and a projection searching algorithm for the extraction of license plates. The recognition result is obtained from a character recognition based on template matching. The proposed work in this paper is the first attempt towards Macao-style license plates.
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paper_title: Detection and recognition of license plate characters with different appearances

paper_content:

This paper proposes an approach to developing an automatic license plate recognition system. Car images are taken from various positions outdoors. Because of the variations of angles from the camera to the car, license plates have various locations and rotation angles in an image. In the license plate detection phase, the magnitude of the vertical gradients is used to detect candidate license plate regions. These candidate regions are then evaluated based on three geometrical features: the ratio of width and height, the size and the orientation. The last feature is defined by the major axis. In the character recognition phase, we must detect character features that are non-sensitive to the rotation variations. The various rotated character images of a specific character can be normalized to the same orientation based on the major axis of the character image. The crossing counts and peripheral background area of an input character image are selected as the features for rotation-free character recognition. Experimental results show that the license plates detection method can correctly extract all license plates from 102 car images taken outdoors and the rotation-free character recognition method can achieve an accuracy rate of 98.6%.
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paper_title: Research on Vehicle License Plate Location Based on Neural Networks

paper_content:

There are some usual methods in vehicle license plate location, such as segmentation in grey-level image, color image edge extraction and neural networks filters etc. All these methods are proved not quite satisfactory in various conditions, or are influenced by some factors. In this paper, we present to classify colors of pixels by using improved neural networks, which include 27 nodes of input layer, 30 nodes of hidden layer and 6 nodes of output layer. Several candidate plate regions are extracted from the results of classification. Then a criterion including the features of areas, the ratios of width to height and vertical projection histogram is proposed to decide a real license plate region. Experimental results show that this method has a high locating rate, and adapts to various conditions.
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paper_title: A novel license plate localization method based on textural feature analysis

paper_content:

Automatic license plate localization in real-time environment is a complex task, due to the variety of sizes and colors as well as to the complexity of the background. In this paper we address this problem by proposing a novel localization scheme. The proposed approach is designed to work in a wide range of acquisition conditions, including unrestricted scene environments, camera-to-car distance, etc. A four-stage search process is proposed in this paper: pre-processing, approximate region searching, region classification and skew correction using Radon transform. Experiments have been preformed to prove the robustness and accuracy of the approach. The experiment results show that almost 96.1 % of input images are correctly localized on the average
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paper_title: Number plate reading using computer vision

paper_content:

The main tasks of a number plate recognition system are the location of the number plate area in the image, the segmentation of the characters and their identification. These tasks are strongly inter-related, mainly because the way to check if the number plate has been correctly located is based on the result of the character identification process (it should correspond to a predefined syntax). Algorithmic improvements to previous versions of the system, based on the results of intensive testing, are described in this paper. For the location of the number plate area in the image, a new line-based method has been developed. The method, instead of looking for character like shapes in the image, takes advantage of the "signature" of the number plate area in a horizontal cross-section of the image. The method used for the segmentation of the characters is derived from a technique first proposed by Lu (1995). The identification of the characters uses the OCR engine developed by Barroso et al. (1995), based on the critical points method.
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paper_title: A novel adaptive morphological approach for degraded character image segmentation

paper_content:

This work proposes a novel adaptive approach for character segmentation and feature vector extraction from seriously degraded images. An algorithm based on the histogram automatically detects fragments and merges these fragments before segmenting the fragmented characters. A morphological thickening algorithm automatically locates reference lines for separating the overlapped characters. A morphological thinning algorithm and the segmentation cost calculation automatically determine the baseline for segmenting the connected characters. Basically, our approach can detect fragmented, overlapped, or connected character and adaptively apply for one of three algorithms without manual fine-tuning. Seriously degraded images as license plate images taken from real world are used in the experiments to evaluate the robustness, the flexibility and the effectiveness of our approach. The system approach output data as feature vectors keep useful information more accurately to be used as input data in an automatic pattern recognition system.
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paper_title: A novel DSP based real-time character classification and recognition algorithm for car plate detection and recognition

paper_content:

In this paper, a novel character classification and recognition algorithm is developed and experimented on. Our classification and recognition technique is based on the properties of characters' features, including their intersection, line ending and continuity features. The application of our novel algorithm to car licence number recognition has been tested using the real-time IVP-150 TMS320C50 digital signal processor based video image processing system. In our implementation, a remote camera will first capture the car image, the captured image will then proceed for image segmentation and pre-processing. Subsequently, the licence number will be located and recognised. In general, excellent performance has been obtained and is demonstrated with our experimental results.
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paper_title: Car license plates extraction and recognition based on connected components analysis and HMM decoding

paper_content:

A system for finding and recognizing car license plates is presented. The finding of the plates is based on the analysis of connected components of four different binarizations of the image. No assumptions are made about illumination and camera angle, and only mild assumptions regarding the size of the plate in the image are made. Recognition is performed by means of Hidden Markov Models. Experiments on a database of Spanish number plates show the feasibility of the proposed approach.
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paper_title: Car license plate recognition based on the combination of principal components analysis and radial basis function networks

paper_content:

This paper proposes a car license plate recognition system. First, an approach based on histogram analysis method together with Hough transform is presented for segmentation. It is robust to car plate inclination within 15 degree. Subsequently, in the recognition phase, we use principle components analysis to extract the feature of input images and then feed to neural networks with radial basis function networks is used for symbols recognition. Experimental results show that the PCA method proposed reduces the dimension of image effectively and the recognition performance using RBF networks with 98% is better than back-propagation (BP) with 93%.
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paper_title: Recognition of Car License Plate by Using Dynamical Thresholding Method and Enhanced Neural Networks

paper_content:

In this paper, for the implementation of the recognition system of car license plates, the region extraction algorithm based on the contour tracking and the new enhanced neural networks learning algorithm are proposed, which extracts the areas of car license plate and the character areas from the car images and recognizes the car license numbers from the extracted areas. And a candidate area was selected, whose density rate was corresponding to the properties of the car license plate obtained in the condition of the car license plate. The contour tracking algorithm extracted the feature areas covering the areas of characters from the car license plate. As well, the enhanced neural networks learning algorithm, combining the modified ART1 and supervised learning algorithm, recognized the car license numbers from the feature areas.
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paper_title: License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization

paper_content:

This paper presents a novel algorithm for license plate detection and license plate character segmentation problems by using the Gabor transform in detection and local vector quantization in segmentation. As of our knowledge this is the first application of Gabor filters to license plate segmentation problem. Even though much of the research efforts are devoted to the edge or global thresholding-based approaches, it is more practical and efficient to analyze the image in certain directions and scales utilizing the Gabor transform instead of error-prone edge detection or thresholding. Gabor filter response only gives a rough estimate of the plate boundary. Then binary split tree is used for vector quantization in order to extract the exact boundary and segment the plate region into disjoint characters which become ready for the optical character recognition.
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paper_title: License plate recognition using DTCNNs

paper_content:

Automatic license plate recognition requires a series of complex image processing steps. For practical use, the amount of data to be processed must be minimized at early stage. This paper shows that the computationally most intensive steps can be realized by discrete time cellular neural networks (DTCNNs). Moreover, high-level operations like 'finding the license plate in the image' and 'finding the characters on the plate' need only a small number of DTCNNs. Real-life tests show that the DTCNNs are capable of correctly identifying more than 85% out of all license plates while leaving only 0.5% of the original information to be inspected for actual recognition.
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paper_title: Morphological Image Analysis: Principles and Applications

paper_content:

From the Publisher: ::: The purpose of this book is to provide readers with an in-depth presentation of the principles and applications of morphological image analysis. This is achieved through a step by step process starting from the basic morphological operators and extending to the most recent advances which have proven their practical usefulness. This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.
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paper_title: Vehicle license plate recognition in marketing application

paper_content:

The paper presents an artificial vision system able to analyze the image of a car given by a camera, to locate the registration plate and to recognize its registration number. It describes practical problems encountered in implementing this application and the proposed solutions. The system has been designed using a modular approach. Sub-modules can be upgraded and or substituted independently, thus making the system potentially suitable in a large variety of vision applications. Performances of the system, have been evaluated in real situation.
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paper_title: Efficient unassisted video segmentation using enhanced fast marching

paper_content:

The paper describes a novel unassisted, fully automatic segmentation technique applicable to colour natural video sequences. The algorithm uses the fast marching method for fast extraction of semantic objects from a frame in a video sequence. These objects are extracted by joint motion and colour analysis. The algorithm handles background in the same way as other objects, thus it does not need global motion compensation. The basic disadvantage of the fast marching method is related to unidirectional motion of an active contour. The new technique overcomes this difficulty by use of an enhancement step consisting in colour processing only. This processing step is performed on a small portion a frame only.
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paper_title: Multinational license plate recognition system: segmentation and classification

paper_content:

Image-based car license plate recognition (CLPR) systems provide an inexpensive automatic solution for remote vehicle identification. Localization stage of the CLPR yields a gray-scale plate clip with printed characters. This paper describes the method of plate clip segmentation into isolated characters, feature extraction and classification. The method is independent on character size, thickness, illumination and is capable of handling plates from various countries. The method uses extensively the gray-scale information and is robust to breaks in character connectivity. It is tolerant to character deformations, such as shear and skew. Promising results have been obtained on Israeli and Bulgarian plates.
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paper_title: New Methods for Automatic Reading of VLP's (Vehicle License Plates)

paper_content:

ABSTRACT In a former work, we built a VLP (Vehicle License Plates)reader using computer vision. To test other methods, wedecided to implement a whole new system from scratch. Inthis communication, we summarize the methods that wehave tested and the results we have obtained. Platelocation is based on mathematical morphology andcharacter recognition is implemented using Hausdorffdistance. Results are comparable to those obtained withother methods. KEY WORDS Computer Vision, Morphologic Analysis, TextRecognition, Hausdorff Distance. 1. Introduction This communication describes the development of anautomatic VLP recognizer. Such a system has manypractical applications: security and accounting systems inparking facilities, traffic monitoring, stolen car detectionand other law enforcement applications...To develop this system we cleared our minds and decidedto use methods that were new for us. Our purpose was totest these methods and to compare them to other methodswe had used previously in the same problem [1]. Our nextproject is to merge both systems to get an improved one.The structure of this work is as follows:a) Recognition Algorithms: this part is the core of thepaper and explains the different computer visionmethods. We used mathematical morphology forlocating the plate. We also used image projectionsfor segmenting the caracters (this is a widely usedmethod of O.C.R. technology, but we had neverused it for plate images). Finally, we tested theHausdorff Distance for character recognition(Haussdorf Distance is a well behaved method forcomparing bilevel images).b) Practical Results: here, we see the global resultsachieved by our system. We also compare thissystem to our previous system.c) Future Lines.d) Conclusions.There are other publications that describe VLPrecognizing. For example [2] uses syntactic patternrecognition, [3] also uses morphology for location andneural networks for recognition (they do not publish theirresults), in [4] they use a bunch of heuristical methods(they get good partial results but they do not publish theglobal ones).
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paper_title: A high performance license plate recognition system

paper_content:

This paper presents a powerful automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates was 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.
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paper_title: A real time vehicle's license plate recognition system

paper_content:

A smart and simple algorithm is presented for a vehicle license plate recognition system. Based on pattern matching, this algorithm can be applied for real time detection of license plates for collecting data for surveying or for some application specific purposes. The proposed system has been prototyped using C++ and the experimental results have been shown for recognition of Alberta license plates.
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paper_title: Concurrent Segmentation and Recognition with Shape-Driven Fast Marching Methods

paper_content:

We present a variational framework that integrates the statistical boundary shape models into a Level Set system that is capable of both segmenting and recognizing objects. Since we aim to recognize objects, we trace the active contour and stop it near real object boundaries while inspecting the shape of the contour instead of enforcing the contour to get a priori shape. We get the location of character boundaries and character labels at the system output. We developed a promising local front stopping scheme based on both image and shape information for fast marching systems. A new object boundary shape signature model, based on directional Gauss gradient filter responses, is also proposed. The character recognition system that employs the new boundary shape descriptor outperforms the other systems, based on well-known boundary signatures such as centroid distance, curvature etc.
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paper_title: Robust license plate recognition based on dynamic projection warping

paper_content:

This paper proposes a novel license plate recognition system combined with dynamic projection warping technique, which makes the processed image robust to shifting, paring, and scaling problems. In addition, the method based on scanline checking is applied to exactly extract the position of license plate. Finally, experiment results are given for demonstration.
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paper_title: Localizing Overlapping Parts by Searching the Interpretation Tree

paper_content:

This paper discusses how local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degrees of positional freedom relative to the sensors. The approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of nonoverlapping parts previously described in [18] and [15].
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paper_title: Adaptive local binarization method for recognition of vehicle license plates

paper_content:

A vehicle license-plate recognition system is commonly composed of three essential parts: detecting license-plate region in the acquired images, extracting individual characters, and recognizing the extracted characters. But in the process, the problems like damage of license-plate and unequal light effect make it difficult to detect accurate vehicle license-plate region and to extract letters in that region. In this paper, to extract characters accurately in the license- plate region, a local adaptive binarization method which is robust under non-uniform lighting environment is proposed. To get better binary images, region- based threshold correction based on a prior knowledge of character arrangement in the license-plate is applied. With the proposed binarization method, 96% of 650 sample vehicle license-plates images are correctly recognized. Compared to existing local threshold selection methods, about 5% of improvement in recognition rate is obtained with the same recognition module based on LVQ.
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paper_title: THRESHOLD SELECTION BASED ON A SIMPLE IMAGE STATISTIC

paper_content:

Abstract The problem of automatic threshold selection is considered. After a brief review of available techniques, a novel method is proposed. It is based on image statistics which can be computed without histogramming the grey level values of the image. A detailed analysis of the properties of the algorithm is then carried out. The effectiveness of the method is shown on a number of practical examples.
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paper_title: An Iterative Thresholding Algorithm for Image Segmentation

paper_content:

A thresholding technique is developed for segmenting digital images with bimodal reflectance distributions under nonuniform illumination. The algorithm works in a raster format, thus making it an attractive segmentation tool in situations requiring fast data throughput. The theoretical base of the algorithm is a recursive Taylor expansion of a continuously varying threshold tracking function.
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paper_title: Gray level thresholding in badly illuminated images

paper_content:

The thresholding method involves first locating objects in an image by using the intensity gradient, then noting the levels that correspond to the objects in various areas of the image, and finally using these levels as initial guesses at a threshold. This method is capable of thresholding images that have been produced in the context of variable illumination. The thresholding method, called the local intensity gradient (LIG) method, was implemented in C using a Sun4 host running UNIX. The LIG method was compared against iterative selection (IS), gray level histograms (GLHs) and two correlation based algorithms on a dozen sample images under three different illumination effects. Overall, the LIG method, while it takes significantly longer, properly thresholds a larger set of images than does any other method examined over the sample images tested. >
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paper_title: A NEURAL NETWORK BASED ARTIFICIAL VISION SYSTEM FOR LICENSE PLATE RECOGNITION

paper_content:

This paper describes a neural network based vision system that is able to analyze the image of a car captured by a camera, locate the registration plate and recognize the registration number of the car. The paper also describes the different practical problems encountered in implementing this application and the solutions used to solve them.
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paper_title: Car license plates extraction and recognition based on connected components analysis and HMM decoding

paper_content:

A system for finding and recognizing car license plates is presented. The finding of the plates is based on the analysis of connected components of four different binarizations of the image. No assumptions are made about illumination and camera angle, and only mild assumptions regarding the size of the plate in the image are made. Recognition is performed by means of Hidden Markov Models. Experiments on a database of Spanish number plates show the feasibility of the proposed approach.
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paper_title: A template-guided approach to vehicle surveillance and access control

paper_content:

Video-based surveillance techniques have extended their scope to the field of undervehicle inspection. However, their role is currently restricted to the visualization of the inspection results, while the evaluation of their outcome depends solely on the judgement of the operator. This paper presents an intelligent template-guided approach, which addresses the task of computer-assisted undervehicle inspection and access control. It combines a license plate identification stage which authenticates incoming vehicles and a template-guided undervehicle inspection stage which alerts the operator in case of questionable content alterations.
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paper_title: Application of pattern recognition for Farsi license plate recognition

paper_content:

A heart valve holder is provided for implanting a prosthetic heart valve in either an inflow (mitral or tricuspid) or an outflow (aortic or pulmonary) implantation. The valve holder may be repeatedly sterilized for multiple implantations. The valve holder includes holding members to securely hold the valve, a valve-sizing member to allow the holder to hold valves of various sizes, an over rotation stop to prevent the holder from impinging upon the valve leaflets, alignment members to align the holding members for receiving a valve, and means to rotate the valve without damaging the valve leaflets.
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paper_title: Some experiments on variable thresholding

paper_content:

Abstract Chow and Kaneko proposed a method of variable thresholding in which an image is divided into windows; thresholds are selected for those windows that have bimodal histograms; and these thresholds are interpolated to define a variable threshold for the entire image. This method was applied to several TV images of machine parts; the results obtained appeared to be considerably better than the results of thresholding at a fixed level. An extension of the method was defined that allowed histograms to be either bimodal or trimodal; this yielded some further improvement in the results, but was also more sensitive to shadows. Finally, an adaptive quantization scheme, based on histogram peak sharpening, was applied to two of the images; the results do not seem to be as good as those obtained using variable thresholding.
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paper_title: Robust license-plate recognition method for passing vehicles under outside environment

paper_content:

Novel methods to recognize license plates robustly are presented. A sensing system with a wide dynamic range has been developed to acquire fine images of vehicles under varied illumination conditions. The developed sensing system can expand the dynamic range of the image by combining a pair of images taken under different exposure conditions. In order to avert blurring of images against fast passing vehicles, a prism beam splitter installed a multilayered filter, and two charge-coupled devices are utilized to capture those images simultaneously. Furthermore, to extend the flexibility of camera placement, a recognition algorithm that can be applied to inclined plates has been developed. The performance of recognizing registration numbers on license plates has been investigated on real images of about 1000 vehicles captured under various illumination conditions. Recognition rates of over 99% (conventional plates) and over 97% (highly inclined plates) showed that the developed system is quite effective for license-plate recognition.
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paper_title: PC based number plate recognition system

paper_content:

A PC based number plate recognition system is presented. Digital gray-level images of cars are thresholded using the Niblack algorithm, which was found to outperform all binarization techniques previously used in similar systems. A simple yet highly effective rule-based algorithm detects the position and size of number plates. Characters are segmented from the thresholded plate using blob-colouring, and passed as 15/spl times/15 pixel bitmaps to a neural network based optical character recognition (OCR) system. A novel dimension reduction technique reduces the neural network inputs from 225 to 50 features. Six small networks in parallel are used, each recognising six characters. The system can recognize single and double line plates under varying lighting conditions and slight rotation. Successful recognition of complete registration plates is about 86.1%.
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paper_title: Optical recognition of motor vehicle license plates

paper_content:

A system for the recognition of car license plates is presented. The aim of the system is to read automatically the Italian license number of a car passing through a tollgate. A CCTV camera and a frame grabber card are used to acquire a rear-view image of the vehicle. The recognition process consists of three main phases. First, a segmentation phase locates the license plate within the image. Then, a procedure based upon feature projection estimates some image parameters needed to normalize the license plate characters. Finally, the character recognizer extracts some feature points and uses template matching operators to get a robust solution under multiple acquisition conditions. A test has been done on more than three thousand real images acquired under different weather and illumination conditions, thus obtaining a recognition rate close to 91%. >
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paper_title: License Plate Character Segmentation Using Hidden Markov Chains

paper_content:

We propose a method for segmentation of a line of characters in a noisy low resolution image of a car license plate. The Hidden Markov Chains are used to model a stochastic relation between an input image and a corresponding character segmentation. The segmentation problem is expressed as the maximum a posteriori estimation from a set of admissible segmentations. The proposed method exploits a specific prior knowledge available for the application at hand. Namely, the number of characters is known and its is also known that the characters can be segmented to sectors with equal but unknown width. The efficient algorithm for estimation based on dynamic programming is derived. The proposed method was successfully tested on data from a real life license plate recognition system.
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paper_title: A spatial thresholding method for image segmentation

paper_content:

Several model-based algorithms for threshold selection are presented, concentrating on the two-population univariate case in which an image contains an object and background. It is shown how the main ideas behind two important nonspatial thresholding algorithms follow from classical discriminant analysis. Novel thresholding algorithms that make use of available local/spatial information are then given. It is found that an algorithm using alternating mean thresholding and median filtering provides an acceptable method when the image is relatively highly contaminated, and seems to depend less on initial values than other procedures. The methods are also applicable to multispectral k-population images. >
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paper_title: Ten Lectures on Statistical and Structural Pattern Recognition

paper_content:

Preface. Lecture 1. Bayesian statistical decision making. Lecture 2. Non-Bayesian statistical decision making. Lecture 3. Two statistical models of the recognised object. Lecture 4. Learning in pattern recognition. Lecture 5. Linear discriminant function. Lecture 6. Unsupervised Learning. Lecture 7. Mutual relationship of statistical and structural recognition. Lecture 8. Recognition of Markovian sequences. Lecture 9. Regular languages and corresponding pattern recognition tasks. Lecture 10. Context-free languages, their 2-D generalisation, related tasks. Bibliography. Index.
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paper_title: Extracting characters of license plates from video sequences

paper_content:

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle, given a sequence of perspective-distortion-corrected license plate images. Different from many existing single-frame approaches, our method simultaneously utilizes spatial and temporal information. We first model the extraction of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing a posteriori probability based on a given prior knowledge and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted and some of our experimental results are presented in the paper. It is shown that our approach provides better performance than other single frame methods.
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paper_title: On combining classifiers

paper_content:

We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions-the sum rule-outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show that this finding can be justified theoretically.
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paper_title: Adaptive local binarization method for recognition of vehicle license plates

paper_content:

A vehicle license-plate recognition system is commonly composed of three essential parts: detecting license-plate region in the acquired images, extracting individual characters, and recognizing the extracted characters. But in the process, the problems like damage of license-plate and unequal light effect make it difficult to detect accurate vehicle license-plate region and to extract letters in that region. In this paper, to extract characters accurately in the license- plate region, a local adaptive binarization method which is robust under non-uniform lighting environment is proposed. To get better binary images, region- based threshold correction based on a prior knowledge of character arrangement in the license-plate is applied. With the proposed binarization method, 96% of 650 sample vehicle license-plates images are correctly recognized. Compared to existing local threshold selection methods, about 5% of improvement in recognition rate is obtained with the same recognition module based on LVQ.
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paper_title: Reading car license plates by the use of artificial neural networks

paper_content:

For evaluating parking rates without the usage of tickets the reading of car license plates from video image sequences taking from the fronts of incoming cars was implemented under use of artificial neural networks (ANNs). The detection of the number plate area and the classification of the classically separated character regions were implemented by different ANN approaches with convincing results.
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paper_title: Learning-based approach for license plate recognition

paper_content:

Presents a learning-based approach for the construction of a license-plate recognition system. The system consists of three modules. They are, respectively, the car detection module, the license-plate segmentation module and the recognition module. The car detection module detects a car in a given image sequence obtained from a camera with a simple color-based approach. The segmentation module extracts the license plate in the detected car image using neural networks as filters for analyzing the color and texture properties of the license plate. The recognition module then reads the characters on the detected license plate with a support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from toll-gates, parking lots, etc., and has shown the following performances on average: car detection rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%.
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paper_title: A practical license plate recognition system for real-time environments

paper_content:

A computer vision system to recognize license plates of vehicles in real-time environments is presented in this study. The images of moving vehicles are taken with a digital camera and analyzed in real-time. An artificial neural network (ANN) system is used to locate the area and position of the license plate. The system has the following stages: (i) Image acquisition and determination of the location of the vehicle license plate (VLP), (ii) segmentation of the VLP into separate characters using image processing techniques, and (iii) recognition of each symbol in VLP using a feedforward artificial neural network (ANN) and assembly of the characters. Performance results are presented at the end.
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paper_title: Car license plate recognition with neural networks and fuzzy logic

paper_content:

A car license plate recognition system (CLPR-system) has been developed to identify vehicles by the contents of their license plate for speed-limit enforcement. This type of application puts high demands on the reliability of the CLPR-system. A combination of neural and fuzzy techniques is used to guarantee a very low error rate at an acceptable recognition rate. First experiments along highways in the Netherlands show that the system has an error rate, of 0.02% at a recognition rate of 98.51%. These results are also compared with other published CLPR-systems.
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paper_title: A hybrid method for robust car plate character recognition

paper_content:

Image-based car plate recognition is an indispensable part of an intelligent traffic system. The quality of the images taken for car plates, especially for Chinese car plates, however, may sometimes be very poor, due to the operating conditions and distortion because of poor photographical environments. Furthermore, there exist some ''similar'' characters, such as ''8'' and ''B'', ''7'' and ''T'' and so on. They are less distinguishable because of noises and/or distortions. To achieve robust and high recognition performance, in this paper, a two-stage hybrid recognition system combining statistical and structural recognition methods is proposed. Car plate images are skew corrected and normalized before recognition. In the first stage, four statistical sub-classifiers recognize the input character independently, and the recognition results are combined using the Bayes method. If the output of the first stage contains characters that belong to prescribed sets of similarity characters, structure recognition method is used to further classify these character images: they are preprocessed once more, structure features are obtained from them and these structure features are fed into a decision tree classifier. Finally, genetic algorithm is employed to achieve optimum system parameters. Experiments show that our recognition system is very efficient and robust. As part of an intelligent traffic system, the system has been in successful commercial use.
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paper_title: Using sliding concentric windows for license plate segmentation and processing

paper_content:

In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding windows) in conjunction with a character recognition neural network. The algorithm was tested with 2820 natural scene gray level vehicle images of different backgrounds and ambient illumination. The camera focused on the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 2719 over 2820 input images (96.4%). The optical character recognition (OCR) system is a two layer probabilistic neural network with topology 108-180-36, whose performance reached 97.4%. The PNN was trained to identify multi-font alphanumeric characters from car license plates based on data obtained from algorithmic image processing.
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paper_title: A novel feature extraction method and hybrid tree classification for handwritten numeral recognition

paper_content:

A hybrid classification system with neural network and decision tree as the classifiers for handwritten numeral recognition is proposed. Firstly a variety of stable and reliable global features are defined and extracted based on the character geometric structures, a novel floating detector is then proposed to detect segments along the left and right profiles of a character image used as local features. The recognition system consists of a hierarchical coarse classification and fine classification. For the coarse classifier: a three-layer feed forward neural network with back propagation learning algorithm is employed to distinguish six subsets {0}, {6}, {8}, {1,7}, {2, 3, 5}, {4, 9} based on the feature similarity of characters extracted. Three character classes namely {0}, {6} and {8} are directly recognized from artificial neural network (ANN). For each of characters in the latter three subsets, a decision tree classifier is built for further fine classification as follows: Firstly, the specific feature-class relationship is heuristically and empirically deduced between the feature primitives and corresponding semantic class. Then, an iterative growing and pruning algorithm is used to form a tree classifier. Experiments demonstrated that the proposed recognition system is robust and flexible and a high recognition rate is reported.
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paper_title: Extraction of car license plate using motor vehicle regulation and character pattern recognition

paper_content:

Extraction of car license plate is important for identifying a car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images can be distorted and the car license plate difficult to extract. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information and shape are applied to determine the candidates of car license plates. To certify the license plate, the characters, numbers and their patterns are recognized by backpropagation neural networks in windows which are opened in those boundaries of candidates. For the results of recognition by neural networks, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. Since the results of characters-pattern recognition is used to certify the license plate, the ability of license-plate extracting is enhanced and the car is identified simultaneously. The results of the experiments with 70 samples of real car images show the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.
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paper_title: A Neural Network Architecture for Visual Selection

paper_content:

This article describes a parallel neural net architecture for efficient and robust visual selection in generic gray-level images. Objects are represented through flexible star-type planar arrangements of binary local features which are in turn star-type planar arrangements of oriented edges. Candidate locations are detected over a range of scales and other deformations, using a generalized Hough transform. The flexibility of the arrangements provides the required invariance. Training involves selecting a small number of stable local features from a predefined pool, which are well localized on registered examples of the object. Training therefore requires only small data sets. The parallel architecture is constructed so that the Hough transform associated with any object can be implemented without creating or modifying any connections. The different object representations are learned and stored in a central module. When one of these representations is evoked, it "primes" the appropriate layers in the network so that the corresponding Hough transform is computed. Analogies between the different layers in the network and those in the visual system are discussed. Furthermore, the model can be used to explain certain experiments on visual selection reported in the literature.
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paper_title: A tutorial on hidden Markov models and selected applications in speech recognition

paper_content:

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
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paper_title: A coarse-to-fine strategy for multiclass shape detection

paper_content:

Multiclass shape detection, in the sense of recognizing and localizing instances from multiple shape classes, is formulated as a two-step process in which local indexing primes global interpretation. During indexing a list of instantiations (shape identities and poses) is compiled, constrained only by no missed detections at the expense of false positives. Global information, such as expected relationships among poses, is incorporated afterward to remove ambiguities. This division is motivated by computational efficiency. In addition, indexing itself is organized as a coarse-to-fine search simultaneously in class and pose. This search can be interpreted as successive approximations to likelihood ratio tests arising from a simple ("naive Bayes") statistical model for the edge maps extracted from the original images. The key to constructing efficient "hypothesis tests" for multiple classes and poses is local ORing; in particular, spread edges provide imprecise but common and locally invariant features. Natural tradeoffs then emerge between discrimination and the pattern of spreading. These are analyzed mathematically within the model-based framework and the whole procedure is illustrated by experiments in reading license plates.
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paper_title: The recognition of car license plate for automatic parking system

paper_content:

The recognition of a car's license plate for an automatic parking system is important for identifying the car at the entrance of the parking area because the car license plate has unique information for each car. This paper proposes the recognition of car license plate which is accurate and robust to environmental variation by using the car's license plate patterns according to motor vehicle regulation and a 4-layer BP neural network with supervised learning. In this method, the candidates regions of the car license plate are determined approximately according to the car license plate regulation such as color, the ratio and shape of the car license plate, the pattern of characters and numbers etc. For the results of recognition by neural networks, the candidate that has characters and numbers according to motor vehicle regulation is certified as license-plate region. Since the results of characters-pattern recognition are used to certify the license plate, the ability of license plate extraction is more accurate and the car can be identified simultaneously. The experimental results of seventy car images with the prototype of the automatic parking system show the performance of car license plate extraction rate of 96%, and the recognition rate is 92%.
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paper_title: Recognition of handwritten numerals with multiple feature and multistage classifier

paper_content:

Abstract Multiple experts system is shown to be a promising strategy for handwritten recognition. This paper presents a multiple experts system using neural networks. In the proposed system, the authors have developed: (1) an incremental clustering neural network algorithm with merging and canceling process, (2) a modified directional histogram feature extraction method, and (3) a subclass method with learning rejection neuron strategy. Our experimental results on a large set of data show the efficiency and robustness of the proposed system.
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paper_title: A Computational Model for Visual Selection

paper_content:

We propose a computational model for detecting and localizing instances from an object class in static gray-level images. We divide detection into visual selection and final classification, concentrating on the former: drastically reducing the number of candidate regions that require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings that are moderately likely at certain places on the object but rate in the background. We show that the statistics in both populations are stable. The candidate regions are those that contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such as responses to coarse or incomplete patterns (e.g., illusory contours) and to scale and translation invariance in IT. Finally, the algorithm is applied to face and symbol detection.
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paper_title: Car license plates extraction and recognition based on connected components analysis and HMM decoding

paper_content:

A system for finding and recognizing car license plates is presented. The finding of the plates is based on the analysis of connected components of four different binarizations of the image. No assumptions are made about illumination and camera angle, and only mild assumptions regarding the size of the plate in the image are made. Recognition is performed by means of Hidden Markov Models. Experiments on a database of Spanish number plates show the feasibility of the proposed approach.
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paper_title: Recognition of Car License Plate by Using Dynamical Thresholding Method and Enhanced Neural Networks

paper_content:

In this paper, for the implementation of the recognition system of car license plates, the region extraction algorithm based on the contour tracking and the new enhanced neural networks learning algorithm are proposed, which extracts the areas of car license plate and the character areas from the car images and recognizes the car license numbers from the extracted areas. And a candidate area was selected, whose density rate was corresponding to the properties of the car license plate obtained in the condition of the car license plate. The contour tracking algorithm extracted the feature areas covering the areas of characters from the car license plate. As well, the enhanced neural networks learning algorithm, combining the modified ART1 and supervised learning algorithm, recognized the car license numbers from the feature areas.
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paper_title: Probabilistic neural networks

paper_content:

Abstract By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network (PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware “neurons” that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation.
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paper_title: License plate recognition using DTCNNs

paper_content:

Automatic license plate recognition requires a series of complex image processing steps. For practical use, the amount of data to be processed must be minimized at early stage. This paper shows that the computationally most intensive steps can be realized by discrete time cellular neural networks (DTCNNs). Moreover, high-level operations like 'finding the license plate in the image' and 'finding the characters on the plate' need only a small number of DTCNNs. Real-life tests show that the DTCNNs are capable of correctly identifying more than 85% out of all license plates while leaving only 0.5% of the original information to be inspected for actual recognition.
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paper_title: Vehicle license plate recognition in marketing application

paper_content:

The paper presents an artificial vision system able to analyze the image of a car given by a camera, to locate the registration plate and to recognize its registration number. It describes practical problems encountered in implementing this application and the proposed solutions. The system has been designed using a modular approach. Sub-modules can be upgraded and or substituted independently, thus making the system potentially suitable in a large variety of vision applications. Performances of the system, have been evaluated in real situation.
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paper_title: A probabilistic framework for combining multiple classifiers at abstract level

paper_content:

Most previous studies assumed that classifiers behave independently. Such an assumption degrades and biases classification performance in the case of adding highly dependent classifiers. In order to overcome such a weakness, one should consider combining multiple classifiers in a probabilistic framework using a Bayesian formalism without the assumption. The probabilistic combination of K classifiers needs a (K+1)st-order probability distribution. However, it as well known that the distribution becomes unmanageable when storing and estimating, even for small K. Chow and Liu (1968) as well as Lewis (1959) proposed a product approximation of a high order distribution with a set of only first-order tree dependencies or second-order distributions. However, if a classifier follows more than one classifier, such a first-order dependency will not be suitable to approximate the high order distribution properly. A probabilistic framework is proposed to identify an optimal product set of kth-order dependencies for an approximation of the (K+1)st-order probability distribution where 1/spl les/k/spl les/K, and to combine K decisions by the identified product set using a Bayesian formalism. This framework was experimented and evaluated with a standardized CENPARMI database and showed superior performance when compared to other combination methods.
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paper_title: Application of pattern recognition for Farsi license plate recognition

paper_content:

A heart valve holder is provided for implanting a prosthetic heart valve in either an inflow (mitral or tricuspid) or an outflow (aortic or pulmonary) implantation. The valve holder may be repeatedly sterilized for multiple implantations. The valve holder includes holding members to securely hold the valve, a valve-sizing member to allow the holder to hold valves of various sizes, an over rotation stop to prevent the holder from impinging upon the valve leaflets, alignment members to align the holding members for receiving a valve, and means to rotate the valve without damaging the valve leaflets.
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paper_title: Competitive Learning: From Interactive Activation to Adaptive Resonance

paper_content:

Functional ond mechanistic comparisons are mode between several network models of cognitive processing: competitive learning, interactive activation, adaptive resonance, and back propagation. The starting point of this comparison is the article of Rumelhart ond Zipser (1985) on feature discovery through competitive learning. All the models which Rumelhart and Zipser (1985) have described were shown in Grossberg (1976b) to exhibit a type of learning which is temporally unstable. Competitive learning mechanisms con be stabilized in response to an arbitrary input environment by being supplemented with mechanisms for learning top-down expectancies, or templates; for matching bottom-up input patterns with the top-down expectancies; and for releasing orienting reactions in o mismatch situation, thereby updating short-term memory ond searching for another internal representation. Network architectures which embody all of these mechonisms were called adaptive resonance models by Grossberg (1976~). Self-stabilizing learning models are candidates for use in real-world applications where unpredictable changes can occur in complex input environments. Competitive learning postulates ore inconsistent with the postulates of the interactive activotion model of McClelland and Rumelhart (1981). and suggest different levels of processing and interaction rules for the analysis of word recognition. Adaptive resonance models use these alternative levels and interaction rules. The selforganizing learning of on odaptive resonance model is compared ond contrasted with the teacher-directed learning of a back propagation model. A number of criteria for evaluating reol-time network models of cognitive processing ore described and applied.
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paper_title: Feature extraction methods for character recognition-A survey

paper_content:

This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.
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paper_title: A novel approach for license plate recognition using subspace projection and probabilistic neural network

paper_content:

License plate recognition has many applications in traffic systems. It is very difficult because images are usually noisy, broken or incomplete. In this paper, a novel robust approach for license plate recognition is proposed, which combines subspace projection with probabilistic neural network to improve the recognition rate. Probabilistic neural network is used as a classifier to identify low-dimension test samples which are obtained from actual license plate images by subspace projection. Experiment results show the effectiveness of the proposed method.
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paper_title: PC based number plate recognition system

paper_content:

A PC based number plate recognition system is presented. Digital gray-level images of cars are thresholded using the Niblack algorithm, which was found to outperform all binarization techniques previously used in similar systems. A simple yet highly effective rule-based algorithm detects the position and size of number plates. Characters are segmented from the thresholded plate using blob-colouring, and passed as 15/spl times/15 pixel bitmaps to a neural network based optical character recognition (OCR) system. A novel dimension reduction technique reduces the neural network inputs from 225 to 50 features. Six small networks in parallel are used, each recognising six characters. The system can recognize single and double line plates under varying lighting conditions and slight rotation. Successful recognition of complete registration plates is about 86.1%.
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paper_title: A template-based model for license plate recognition

paper_content:

Using video camera to manage the cars is gradually adopted in many lands of applications, such as electric payment in the tailgate and car parking management. An automatic recognition model of automobile's license plate number is proposed in this paper. The designed system is expected to have high recognition accuracy and reliability such that the goal of automatic recognition can be achieved. We present a system to recognize the license number in the acquired image captured from a video camera. The recognition process of our system contains four major steps. First, the system tries to locate the probable position of the license plate within the acquired image by using gradient analysis and image processing. Second, our model estimates the image parameters needed to normalize the license plate and uses the cross-correlation to detect the skew of the license plate and rectify the tilt. Third, we use a template technique to recognize the characters in the license plate. Finally, we use the information gained from the previous step to analyze the probable license numbers. We illustrate the designing processes and give the experimental results from the proposed model. Based on the experimental results, the proposed system can effectively recognize the license number. The time needed to recognize a license plate takes only 1.5 seconds.
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paper_title: A Macao license plate recognition system

paper_content:

A license plate recognition (LPR) system plays an important role in numerous applications, such as parking accounting systems, traffic law enforcement, road monitoring and security systems. This paper proposes an experimental license plate recognition system for Macao-style license plates. The system uses morphological operations and a projection searching algorithm for the extraction of license plates. The recognition result is obtained from a character recognition based on template matching. The proposed work in this paper is the first attempt towards Macao-style license plates.
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paper_title: FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators

paper_content:

Modern FPGA platforms provide the hardware and software infrastructure for building a bus-based system on chip (SoC) that meet the applications requirements. The designer can customize the hardware by selecting from a large number of pre-defined peripherals and fixed IP functions and by providing new hardware, typically expressed using RTL. Hardware accelerators that provide application-specific extensions to the computational capabilities of a system is an efficient mechanism to enhance the performance and reduce the power dissipation. What is missing is an integrated approach to identify the computationally critical parts of the application and to create accelerators starting from a high level representation with a minimal design effort. In this paper, we present an automation methodology and a tool that generates accelerators. We apply the methodology on an FPGA-based license plate recognition (LPR) system used in law enforcement. The accelerators process streaming data and support a programming model which can naturally express a large number of embedded applications resulting in efficient hardware implementations. We show that we can achieve an overall LPR application speed up from 1.2/spl times/ to 2.6/spl times/, thus enabling real-time functionality under realistic road scenes.
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paper_title: Number plate reading using computer vision

paper_content:

The main tasks of a number plate recognition system are the location of the number plate area in the image, the segmentation of the characters and their identification. These tasks are strongly inter-related, mainly because the way to check if the number plate has been correctly located is based on the result of the character identification process (it should correspond to a predefined syntax). Algorithmic improvements to previous versions of the system, based on the results of intensive testing, are described in this paper. For the location of the number plate area in the image, a new line-based method has been developed. The method, instead of looking for character like shapes in the image, takes advantage of the "signature" of the number plate area in a horizontal cross-section of the image. The method used for the segmentation of the characters is derived from a technique first proposed by Lu (1995). The identification of the characters uses the OCR engine developed by Barroso et al. (1995), based on the critical points method.
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paper_title: Comparing images using the hausdorff distance

paper_content:

The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image. >
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paper_title: Efficiently Locating Objects Using the Hausdorff Distance

paper_content:

The Hausdorff distance is a measure defined between two point sets, here representing a model and an image. The Hausdorff distance is reliable even when the image contains multiple objects, noise, spurious features, and occlusions. In the past, it has been used to search images for instances of a model that has been translated, or translated and scaled, by finding transformations that bring a large number of model features close to image features, and vice versa. In this paper, we apply it to the task of locating an affine transformation of a model in an image; this corresponds to determining the pose of a planar object that has undergone weak-perspective projection. We develop a rasterised approach to the search and a number of techniques that allow us to locate quickly all transformations of the model that satisfy two quality criteria; we can also efficiently locate only the best transformation. We discuss an implementation of this approach, and present some examples of its use.
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paper_title: Optical recognition of motor vehicle license plates

paper_content:

A system for the recognition of car license plates is presented. The aim of the system is to read automatically the Italian license number of a car passing through a tollgate. A CCTV camera and a frame grabber card are used to acquire a rear-view image of the vehicle. The recognition process consists of three main phases. First, a segmentation phase locates the license plate within the image. Then, a procedure based upon feature projection estimates some image parameters needed to normalize the license plate characters. Finally, the character recognizer extracts some feature points and uses template matching operators to get a robust solution under multiple acquisition conditions. A test has been done on more than three thousand real images acquired under different weather and illumination conditions, thus obtaining a recognition rate close to 91%. >
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paper_title: Analysis of Features for Rigid Structure Vehicle Type Recognition

paper_content:

We describe an investigation into feature representations for rigid structure recognition framework for recognition of objects with a multitude of classes. The intended application is automatic recognition of vehicle type for secure access and traffic monitoring applications, a problem not hitherto considered at such a level of accuracy. We demonstrate that a relatively simple set of features extracted from sections of car front images can be used to obtain high performance verification and recognition of vehicle type (both car model and class). We describe the approach and resulting system in full, and the results of experiments comparing a wide variety of different features. The final system is capable of recognition rates of over 93% and verification equal error rates of fewer than 5.6% when tested on over 1000 images containing 77 different classes. The system is shown to be robust for a wide range of weather and lighting conditions.
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paper_title: Real-time automatic vehicle management system using vehicle tracking and car plate number identification

paper_content:

This paper proposes a real-time vehicle management system using a vehicle tracking and a car plate number identification technique. The system uses two cameras: one for tracking vehicles and another for capturing LP (license plate). We track the vehicles by applying the CONDENSATION algorithm over the vehicle's movement image captured from the first camera. To render the CONDENSATION algorithm more effective, we build a discrete vehicle shape model by training vehicle patterns with a SOM (self organizing map), which makes the system suitable for real-time application. Next, we take the probabilistic dynamic model such as HMM (hidden Markov model) to reflect the temporal change in shape of various vehicles. As a vehicle reaches the designated target line, a signal is sent to the second camera for capturing the vehicle's front side. The captured image is transferred to an LPR (vehicle LP recognition system) which recognizes the vehicle's category and LP. LPR system detects the vehicle LP using the only the vertical edge of the captured vehicle image, and effectively accomplishes the character segmentation of the LP region using the geometric transformation without respect to the position and angle of the CCD camera. The segmented characters are recognized using the SVM (support vector machine). By combining these two techniques, we construct a real-time automatic vehicle management system that can be used to control vehicle parking and searching for specific vehicles.
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paper_title: A Binocular License Plate Reader for High Precision Speed Measurement

paper_content:

The paper describes a vision processing system for traffic speed computation. Vehicle tracking is performed by using high-level features, that are clusters of license plate characters to achieve increased robustness of the system. By using a binocular arrangement of the Computer Vision system, the spatial and temporal range of image capture between consecutive views is extended, which leads to an improved accuracy in speed computation. Multiple views can be collected, from the same vehicle in transit, depending on the amount of speed. The effectiveness of the proposed solution is demonstrated through a geometric simulation model, where almost all operating conditions and constraints are fully exploited and tested. An error sensitivity analysis is carried out, to identify the most critical components of the system and the possible sources of errors in speed computation. This simulation approach is proved quite useful in this complex scenario, where it is commonly very difficult to collect true speed measures from the vehicles in transit. Beside the simulation analysis, a series of experimental results have been collected by a first prototype that is available since the end of year 2000.
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paper_title: A template-guided approach to vehicle surveillance and access control

paper_content:

Video-based surveillance techniques have extended their scope to the field of undervehicle inspection. However, their role is currently restricted to the visualization of the inspection results, while the evaluation of their outcome depends solely on the judgement of the operator. This paper presents an intelligent template-guided approach, which addresses the task of computer-assisted undervehicle inspection and access control. It combines a license plate identification stage which authenticates incoming vehicles and a template-guided undervehicle inspection stage which alerts the operator in case of questionable content alterations.
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paper_title: Neural network approach to identify model of vehicles

paper_content:

The number of vehicles is rapidly increased as modern industrialization is developed worldwide. Vehicle recognition has been studied for some time because many people acknowledged it is critical information to solve vehicle-related problems. However, few researchers have tried to recognize the model of vehicles. In this paper a novel method is proposed to recognize vehicles’ model corresponding manufacturers in order to increase the efficiency of recognition. Texture descriptors are computed from the front image of vehicles. Then, a three-layer neural network was built and trained with the texture descriptors for model recognition. The proposed method demonstrates 94% recognition rate for moving vehicles’ models.
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paper_title: Intelligent traffic management through MPEG-7 vehicle flow surveillance

paper_content:

Recent development in the aspects of low-level image processing for feature extraction, as well as in the standardization of description schemes for video description, provide means for the development of Intelligent Transportation Systems. ITS may support a wide range of abilities, including vehicle identification and reidentification, event detection and optimum resource management. This paper analyzes the structure operation principles and the structure of an integrated highway management system. Its operation is based on the combination of sensor networks, low-level image processing algorithms and high-level description schemes. Low-level algorithms perform the tasks of license plate recognition for vehicle identification, as well as feature extraction through change detection, for event detection purposes. High level schemes are based on the recently developed MPEG-7 Description Schemes and are utilized as an information processing framework, which formulates identification and event representation procedures.
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paper_title: Implementation of embedded system for vehicle tracking and license plates recognition using spatial relative distance

paper_content:

This paper set out to use a camera attached to a mobile terminal in order to inquire a car and track its location anywhere. The technology based in the research is to automatically recognize and verify license plates on the front and back of a car. The plates are input through a camera attached to a mobile terminal. The technology enables us to detect a car registration number and to transmit it along with the location of the terminal to a server through a wireless communication network. The information of a car obtained through the terminal is encoded and transmitted to a server in a remote place through a wireless communication network. The car registration number and its location information are decoded and transmitted as a text to the server in a remote place. In order to track a user's location through spatial relative distance estimated in real-time, the server uses the spatial and attribute information which are the most prior to the desired data value. With this property information, the right location can be calculated
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paper_title: Vehicle License Plate Segmentation in Natural Images

paper_content:

A robust method for plate segmentation in a License Plate Recognition (LPR) system is presented, designed to work in a wide range of acquisition conditions, including unrestricted scene environments, light, perspective and camera-to-car distance. Although this novel text-region segmentation technique has been applied to a very specific problem, it is extensible to more general contexts, like difficult text segmentation tasks dealing with natural images. Extensive experimentation has been performed in order to estimate the best parameters for the task at hand, and the results obtained are presented.
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paper_title: Color Texture-Based Object Detection: An Application to License Plate Localization

paper_content:

This paper presents a novel color texture-based method for object detection in images. To demonstrate our technique, a vehicle license plate (LP) localization system is developed. A support vector machine (SVM) is used to analyze the color textural properties of LPs. No external feature extraction module is used, rather the color values of the raw pixels that make up the color textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, LP regions are identified by applying a continuously adaptive meanshift algorithm (CAMShift) to the results of the color texture analysis. The combination of CAMShift and SVMs produces not only robust and but also efficient LP detection as time-consuming color texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be analyzed.
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paper_title: Learning-based approach for license plate recognition

paper_content:

Presents a learning-based approach for the construction of a license-plate recognition system. The system consists of three modules. They are, respectively, the car detection module, the license-plate segmentation module and the recognition module. The car detection module detects a car in a given image sequence obtained from a camera with a simple color-based approach. The segmentation module extracts the license plate in the detected car image using neural networks as filters for analyzing the color and texture properties of the license plate. The recognition module then reads the characters on the detected license plate with a support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from toll-gates, parking lots, etc., and has shown the following performances on average: car detection rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%.
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paper_title: An Efficient Features - Based License Plate Localization Method

paper_content:

This paper presents a feature-based license plate localization algorithm that copes with multi-object problem in different image capturing conditions. The proposed algorithm is robust against illumination, shadow, scale, rotation, and weather condition. It extracts license plate candidates using edge statistics and morphological operations and removes the incorrect candidates according to the determined features of license plates. We have formed a rather complete database of 269 images in different conditions. The proposed algorithm successfully detecteds the accurate location of the license plates in 96.5% cases, which outperforms the other available approaches in the literature.
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paper_title: Generative Models for License Plate Recognition by using a Limited Number of Training Samples

paper_content:

Increased mobility and internationalization open new challenges to develop effective traffic monitoring and control systems. This is true for automatic license plate recognition architectures that, nowadays, must handle plates from different countries with different character sets and syntax. While much emphasis has been put on the license plate localization and segmentation, little attention has been devoted to the huge amount of samples that are needed to train the character recognition algorithms. Nevertheless, these samples are difficult to get when dealing with an international-wide scenario that involves many different countries and the related legislations. This paper reports a new algorithm for license plate recognition, developed under a joint research funded by Autostrade per 1'Italia S.p.A., the main Italian highways company. The research aimed at achieving improved recognition rates when dealing with vehicles coming from different European and nearby states. Extensive experimental tests have been performed on a database of about 7.000 images comprising License Plates picked up by portals spread nationally. The overall rate of correct classification is 98.1%.
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paper_title: Extraction and recognition of license plates of motorcycles and vehicles on highways

paper_content:

A recognition system is proposed to extract and recognize license plates of motorcycles and vehicles on highways. In the first stage, a block-difference method is used to detect moving objects. According to the variance and the similarity of the M/spl times/N blocks defined on two diagonal lines, the blocks are categorized as three kinds: low-contrast, stationary and moving blocks. In the second stage, a screening method based on the projection of edge magnitudes is used to find two peaks in the projection histograms to bound license plates. The scanning lines with low counts can be removed. In the third stage, character images are segmented and recognized. In our experiments, we tested 180 pairs of images. The block-difference method has a 98% success rate and can remove 88% of pixels from an image on average. The screening method has a 94.4% success rate and the character recognition method has a 95.7% precision rate.
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paper_title: Towards a Multinational Car License Plate Recognition System

paper_content:

A full-fledged image-based car license plate recognition (CLPR) system is described in the paper. CLPR provides an inexpensive automatic solution for remote vehicle identification. Gray-level input images are assumed. The localization stage of the CLPR yields a plate clip followed by character segmentation and recognition. The recognition scheme combines adaptive iterative thresholding with a template-matching algorithm. The method is invariant to illumination and is robust to character size and thickness, skew and small character breaks. Promising results have been obtained in the experiments with Israeli and Bulgarian license plates including images of poor quality. Also, the possibility of using an “off-the-shelf” OCR has been explored.
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paper_title: A novel license plate localization method based on textural feature analysis

paper_content:

Automatic license plate localization in real-time environment is a complex task, due to the variety of sizes and colors as well as to the complexity of the background. In this paper we address this problem by proposing a novel localization scheme. The proposed approach is designed to work in a wide range of acquisition conditions, including unrestricted scene environments, camera-to-car distance, etc. A four-stage search process is proposed in this paper: pre-processing, approximate region searching, region classification and skew correction using Radon transform. Experiments have been preformed to prove the robustness and accuracy of the approach. The experiment results show that almost 96.1 % of input images are correctly localized on the average
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paper_title: Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features

paper_content:

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.
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paper_title: Superresolution of License Plates in Real Traffic Videos

paper_content:

In this paper, a novel method to enhance license plate numbers of moving vehicles in real traffic videos is proposed. A high-resolution image of the number plate is obtained by fusing the information derived from multiple, subpixel shifted, and noisy low-resolution observations. The image to be superresolved is modeled as a Markov random field and is estimated from the observations by a graduated nonconvexity optimization procedure. A discontinuity adaptive regularizer is used to preserve the edges in the reconstructed number plate for improved readability. Experimental results are given on several traffic sequences to demonstrate the robustness of the proposed method to potential errors in motion and blur estimates. The method is computationally efficient as all operations can be implemented locally in the image domain
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paper_title: Learning-Based License Plate Detection Using Global and Local Features

paper_content:

This paper presents a license plate detection algor ithm using both global edge features and local Haar -like features. Global classifiers using global statistic al features are constructed through simple learning processes. After applying these global classifiers, most of li cense plate background regions are excluded from further training or testing. Then an AdaBoost learning proc edure is constructed to obtain local classifiers ba sed on selected local Haar-like features. By combining the global and local classifiers, we obtain a cascaded classifier consisting of 6 layers with only 160 features. Ther efore, our cascaded classifier is simple and effici ent. Our proposed cascaded classifier is invariant to the br ightness, colour, size and position of license plat es. Satisfactory detection results showing high detection rate with low false positive rate are demonstrated using a vi deo of moving vehicles. Unlike methods built on either fix ed image backgrounds or fixed cameras, our method does not use any inter-frame information and pre-knowledge. Therefore, our method is more flexible for real-tim e applications.
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paper_title: Multiple license plate detection for complex background

paper_content:

This paper presents a wavelet transform based method for extracting license plates from cluttered images. The proposed system consists of three major stages. First, a wavelet transform based method is used for extracting important contrast features as guides to search for desired license plates. Then, finding a reference line in HL subimage plays an important role to locate the desired license plate region roughly. According to the reference line we can decrease the searching region of license plate and speed up the execution time. The last stage of the method is to locate the license plate accurately by license plate adjustment. More importantly, the proposed detection method can locate multiple plates with different orientations in one image. Since the feature extracted is robust to complex backgrounds, the proposed method works well in extracting differently illuminated and oriented license plates. The average accuracy of detection is 92.4%.
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Title: License Plate Recognition From Still Images and Video Sequences: A Survey

Section 1: Introduction
Description 1: Introduce Intelligent Transportation Systems (ITS) and the core role of License Plate Recognition (LPR) algorithms within ITS, highlighting the basic framework and the necessity for real-time processing capabilities.

Section 2: Scope of This Survey
Description 2: Detail the scope of the survey, including the classification and methodology for LPR techniques along with mentioning the collection of a Greek LP dataset for consistent evaluation.

Section 3: LP Detection
Description 3: Discuss various techniques for detecting the license plate (LP) region, categorizing methods such as binary image processing, global image processing, statistical measurements, hierarchical representations, region segmentation, probabilistic object tracking in videos, and image transformations.

Section 4: Character Segmentation
Description 4: Explore the different methodologies for segmenting characters from the detected LP region including binary image processing techniques, mathematical morphology, contours, and gray-level processing.

Section 5: Character Recognition
Description 5: Cover the algorithms and techniques for recognizing the segmented characters from the LP, including statistical classifiers, neural networks, and pattern/template matching methods.

Section 6: Current Trends
Description 6: Highlight the current trends in LPR research such as vehicle model identification, under-vehicle surveillance, speed estimation, and intelligent traffic management.

Section 7: Outlook for the Future
Description 7: Discuss future challenges and directions in LPR including the need for robust systems capable of handling various environmental conditions and plates of different countries and scenarios.

Section 8: Summary
Description 8: Summarize the survey, providing a comprehensive source of reference for LPR research and emphasizing the importance of a common database for consistent performance evaluation in the scientific community.