Survey on Automatic Vehicle Number Plate Localization
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paper_title: License Plate Recognition Using Support Vector Machine

paper_content:

Automatic license plate recognition (ALPR) system for vehicles is a challenging area of research due to its importance to a wide range of commercial applications. The first and the most important stage for any ALPR system is the localization of the license plate within the image captured by a camera. In this paper proposed approach is present. It has considered the Indian number plates, where rear follows the number plate standards. This system consist of few algorithm like "Feature based number plate Localization" for locating the number plate, "Image Scissoring" algorithm for character segmentation and proposed algorithm for character recognition using Support Vector Machine (SVM).System can recognize single or double line number plate.
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paper_title: Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

paper_content:

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.
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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: Automatic Localization and Recognition of License Plate Characters for Indian Vehicles

paper_content:

Automatic License Plate Recognition (ALPR) is a challenging area of research due to its importance to variety of commercial applications. ALPR systems are widely implemented for automatic ticketing of vehicles at car parking area, tracking vehicles during traffic signal violations and related applications with huge saving of human energy and cost. The overall problem may be subdivided into three distinct key modules: (a) localization of license plate from vehicle image, (b) segmentation of the characters within the license plate and (c) recognition of segmented characters within the license plate. The main function of the module (a) is to find out the potential regions within the image that may contain the license plate. The function of module (b) is to isolate the foreground characters from the background within the detected license plate region. And the function of the module (c) is to recognize the segments in terms of known characters or digits. Though modules (b) and (c) employ most of the traditional methods available to the technologists, module (a) i.e. localization of potential license plate regions(s) from vehicle images is the most challenging task due to the huge variations in size, shape, color, texture and spatial orientations of license plate regions in such images. In general, objective of any ALPR system is to localize potential license plate region(s) from the vehicle images captured through a road-side camera and interpret the segmented characters present therein using an Optical Character Recognition (OCR) system, to get the license number of the vehicle. Again, an ALPR system can have two varieties: on-line ALPR system and off-line ALPR system. In an online ALPR system, the localization and interpretation of license plates take place instantaneously from the incoming video frames, enabling real-time tracking of moving vehicles through the surveillance camera. On the other hand, an offline ALPR system captures the vehicle images and stores them in a centralized data server for further processing, i.e. for interpretation of vehicle license plates. The objective of the current work falls under the second category of ALPR system. In this work, real time vehicle images are captured from a road-side surveillance camera automatically throughout day and night. The images are stored in a centralized data server. A never ending process takes the stored images sequentially and interprets the license number of the vehicle. An innovative idea using statistical distribution of the vertical edges is used for localization of license plate, connected component labeling is used for segmentation of the characters and template matching using an innovative matching technique is used for recognition of the characters. The performance of the system is measured at the three levels, i.e. localization level, segmentation level and recognition level and the result seems to be quite satisfactory.
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paper_title: A Design Flow for Robust License Plate Localization and Recognition in Complex Scenes

paper_content:

In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.
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paper_title: Vehicle Number Plate Detection using Sobel Edge Detection Technique

paper_content:

In this paper we detect the number on vehicle plates in the input image. We use simple color conversion ,edge detection and connector measurement technique. Throughout the whole work, we use masking and smoothing operation. The median filter is used as one of the operators. The best results can be obtained by getting the value of connector components more than 17.Various other methods have been proposed so far but we here present simplest of all and with lesser complexity to detect the numbers. The image is stored in the form of a matrix and the output is displayed in the form of detected numbers. The crux of the work is to use Sobel Edge Detection technique. We present a detection algorithm that employs a novel image descriptor and detects license plate. Using covariance matrix for the same purpose to find out statistical and spatial properties could lead to complexity(3) that arises due to neural network. Instead of these methods and techniques, we have used filter convolution and masking operation to detect the number out of the image(as in vehicle's number plate).To minimize complexity( as in covariance matrix), local variance scores have been used and the unique coefficients have been restructured into a feature vector form and multi-layer neural network(3- 4),(8). Since no explicit similarity or distance computation is required in this framework, it is very easily possible to keep the computational load of the detection process low. Moreover, the complexity involved is very less as compared to that in template matching done by using genetic algorithms(5-6)and neural networks. In the current work, first of all the input image is converted into its corresponding RGB format (2)and appropriate filters are applied onto it. In order to smoothen the edges, the technique of convolution is used. Thereafter, the connected components (mx) are detected. The crux of the work lies in extracting exactly all the characters in the number plate when the number of connected components are more than number 17. Finally, the image is stored in the form of a matrix and the output is displayed in the form of detected numbers.
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paper_title: Morphology Based Approach to Recognize Number Plates in India

paper_content:

Morphology based Approach to Recognize License Plates in India studies the problems that are being faced to recognize Vehicle License Plates (VLP) in Indian conditions. This works provides in depth knowledge about the causes of failure of other VLP recognition methods in Indian conditions. This work is based on mathematical operations called Morphological operations such as Dilation, Erosion etc.. Morphology is a technique in which the shape of the image is to be taken care of. Everything revolves around the shape of the image on which operations are to be performed. Some previous studies for character recognition were also referred and a working MATLAB ( MATLAB by Mathworks ) Program has been created that extracts License plate regions only and displays the actual Characters (Vehicle Registration Number or License plate characters)
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paper_title: Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram

paper_content:

Detecting the region of a license plate is the key component of the vehicle license plate recognition (VLPR) system. A new method is adopted in this paper to analyze road images which often contain vehicles and extract LP from natural properties by finding vertical and horizontal edges from vehicle region. The proposed vehicle license plate detection (VLPD) method consists of three main stages: (1) a novel adaptive image segmentation technique named as sliding concentric windows (SCWs) used for detecting candidate region; (2) color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively; and (3) finally, decomposing candidate region which contains predetermined LP alphanumeric character by using position histogram to verify and detect vehicle license plate (VLP) region. In the proposed method, input vehicle images are commuted into grey images. Then the candidate regions are found by sliding concentric windows. We detect VLP region which contains predetermined LP color by using HSI color model and LP alphanumeric character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.
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paper_title: License plate localization and recognition in camera pictures

paper_content:

This paper presents an approach to license plate localization and recognition. A proposed method is designed to perform recognition of any kind of license plates under any environmental conditions. The main assumption of this method is the ability of recognition of all license plates which can be found in an individual picture. To solve the problem of localization of a license plate two independent methods are used. The first one was based on the connected components analysis and the second one searches for the “signature” of the license plate at the image. Segmentation of characters is performed by using vertical projection of license plate’s image. However, a simple neural network is used to recognize them. Finally, to separate correct license plates from other captions in the picture, during the license plate recognition process, a syntax analysis is used. The proposed approach is discussed together with results obtained on a benchmark data set of license plate pictures. In this paper examples of correct and incorrect results are also presented, as well as possible practical applications of proposed method.
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paper_title: Automated Car License Plate Localization using Wavelet Analysis

paper_content:

etection of vehicle license plate is vital for identifying the vehicle because the license plate has unique information for each vehicle. However, in India, vehicle license plate standards, though they exist, are rarely practiced. Large amount of variations are seen in the parameters of license plate like size of number plate, its location, background and foreground color, etc. which makes the task of number plate localization for recognition more difficult. This paper presents a Wavelet analysis based methodology for precise localization of Indian number plates.
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paper_title: Automatic license plate recognition

paper_content:

Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.
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paper_title: License Plate Recognition Algorithm for Passenger Cars in Chinese Residential Areas

paper_content:

This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.
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paper_title: License plate detection based on genetic neural networks, morphology, and active contours

paper_content:

This paper describes a new method for License Plate Detection based on Genetic Neural Networks, Morphology, and Active Contours. Given an image is divided into several virtual regions sized 10 × 10 pixels, applying several performance algorithms within each virtual region, algorithms such as edge detection, histograms, and binary thresholding, etc. These results are used as inputs for a Genetic Neural Network, which provides the initial selection for the probable situation of the license plate. Further refinement is applied using active contours to fit the output tightly to the license plate. With a small and well-chosen subset of images, the system is able to deal with a large variety of images with real-world characteristics obtaining great precision in the detection.The effectiveness for the proposed method is very high (97%). This method will be the first stage of a surveillance system which takes into account not only the actual license plate but also the model of the car to determine if a car should be taken as a threat.
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paper_title: Vehicle License Plate Detection Method Based on Sliding Concentric Windows and Histogram

paper_content:

Detecting the region of a license plate is the key component of the vehicle license plate recognition (VLPR) system. A new method is adopted in this paper to analyze road images which often contain vehicles and extract LP from natural properties by finding vertical and horizontal edges from vehicle region. The proposed vehicle license plate detection (VLPD) method consists of three main stages: (1) a novel adaptive image segmentation technique named as sliding concentric windows (SCWs) used for detecting candidate region; (2) color verification for candidate region by using HSI color model on the basis of using hue and intensity in HSI color model verifying green and yellow LP and white LP, respectively; and (3) finally, decomposing candidate region which contains predetermined LP alphanumeric character by using position histogram to verify and detect vehicle license plate (VLP) region. In the proposed method, input vehicle images are commuted into grey images. Then the candidate regions are found by sliding concentric windows. We detect VLP region which contains predetermined LP color by using HSI color model and LP alphanumeric character by using position histogram. Experimental results show that the proposed method is very effective in coping with different conditions such as poor illumination, varied distances from the vehicle and varied weather.
---
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: Automated Car License Plate Localization using Wavelet Analysis

paper_content:

etection of vehicle license plate is vital for identifying the vehicle because the license plate has unique information for each vehicle. However, in India, vehicle license plate standards, though they exist, are rarely practiced. Large amount of variations are seen in the parameters of license plate like size of number plate, its location, background and foreground color, etc. which makes the task of number plate localization for recognition more difficult. This paper presents a Wavelet analysis based methodology for precise localization of Indian number plates.
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paper_title: License Plate Recognition Algorithm for Passenger Cars in Chinese Residential Areas

paper_content:

This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.
---
paper_title: License plate localization and recognition in camera pictures

paper_content:

This paper presents an approach to license plate localization and recognition. A proposed method is designed to perform recognition of any kind of license plates under any environmental conditions. The main assumption of this method is the ability of recognition of all license plates which can be found in an individual picture. To solve the problem of localization of a license plate two independent methods are used. The first one was based on the connected components analysis and the second one searches for the “signature” of the license plate at the image. Segmentation of characters is performed by using vertical projection of license plate’s image. However, a simple neural network is used to recognize them. Finally, to separate correct license plates from other captions in the picture, during the license plate recognition process, a syntax analysis is used. The proposed approach is discussed together with results obtained on a benchmark data set of license plate pictures. In this paper examples of correct and incorrect results are also presented, as well as possible practical applications of proposed method.
---
paper_title: Automatic license plate recognition

paper_content:

Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.
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Title: Survey on Automatic Vehicle Number Plate Localization

Section 1: INTRODUCTION
Description 1: This section should introduce the concept of Automatic License Plate Recognition (ALPR), its real-life applications, and the importance of license plate localization in ALPR systems.

Section 2: BASIC CONCEPTS
Description 2: This section should cover the fundamental principles, including standard license plate specifications and an explanation of any vehicle numbering standards.

Section 3: License Plate Specifications
Description 3: Specify the standard format and characteristics of vehicle number plates, using Indian license plates as an example.

Section 4: Challenges in ALPR
Description 4: Outline various challenges encountered in Automatic License Plate Recognition, such as poor image resolution and environmental factors.

Section 5: REVIEW
Description 5: Provide an overview of different methods used for the localization of vehicle number plates.

Section 6: Detection of License Plate Using Edge Information
Description 6: Explain the method of detecting license plates using edge detection techniques, including the preprocessing steps and specific algorithms used.

Section 7: Mathematical Morphology for Extraction of Vehicle Number Plates
Description 7: Describe the methodology based on mathematical morphology, including stages like image enhancement, morphological transformations, and extraction process.

Section 8: Vehicle License Plate Detection Using Sliding Concentric Windows (SCWs)
Description 8: Detail the SCWs method for detecting license plate regions, the stages involved, and the specific techniques used for verification and extraction.

Section 9: License Plate Localization Using Connected Components Analysis
Description 9: Discuss the approach of using connected components analysis for license plate localization, including preprocessing and spot elimination techniques.

Section 10: Automated Car License Plate Localization Using Wavelet Analysis
Description 10: Present the method utilizing wavelet analysis for license plate localization, including steps like horizontal and vertical clipping and energy plotting.

Section 11: License Plate Localization Using Color Information
Description 11: Explain how color information is used for license plate localization, including the process of color space conversion and fuzzification.

Section 12: Number Plate Localization Using Texture Feature
Description 12: Describe the texture-based method for localizing number plates, focusing on edge points and aspect ratio considerations.

Section 13: License Plate Detection Based on Genetic Neural Networks, Morphology, and Active Contours
Description 13: Explain the combined method involving genetic neural networks, morphology, and active contours for detecting license plates, detailing the various stages and techniques used.

Section 14: Method Principle Pros
Description 14: Compare the advantages and fundamental principles of various license plate localization methods.

Section 15: COMPARISON OF EACH OF THE LOCALIZATION METHODS
Description 15: Compare the different localization methods in terms of their advantages, disadvantages, and underlying principles.

Section 16: CONCLUSION & FUTURE WORK
Description 16: Summarize the study, emphasizing the importance of license plate localization, and suggest areas for future research and improvement.