A Survey on Optical Character Recognition System
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paper_title: Survey and bibliography of Arabic optical text recognition

paper_content:

Abstract Research work on Arabic optical text recognition (AOTR), although lagging that of other languages, is becoming more intensive than before and commercial systems for AOTR are becoming available. This paper presents a comprehensive survey and bibliography of research on AOTR, by covering all the research publications on AOTR to which the authors had access. This paper introduces the general topic of optical character recognition (OCR), and highlights the characteristics of Arabic text. It also presents an historical review of the Arabic text recognition systems. Further, this paper reports on the state of the art in AOTR research, and lists the specifications of commercially available systems for AOTR. In this paper, we first underline the capabilities of different AOTR systems, and then introduce a five stage model for AOTR systems and classify research work according to this model. We devote a section to each of the stages of this model: preprocessing, segmentation, feature extraction, classification, and post-processing. In the preprocessing section, we emphasize handling degraded documents, and thinning of Arabic text. In the segmentation section, we discuss methods of segmenting Arabic text and categorize the methods into five general approaches. In the feature extraction and classification sections, we highlight the main techniques and analyze AOTR research works based on those techniques. We then discuss approaches for post-processing and show their relation to the Arabic language. We conclude by pointing problems and directions for future research on AOTR.
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paper_title: Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition

paper_content:

Automatic Number Plate Recognition (ANPR) is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. The system is implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. Parliament, Supreme Court etc. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for the character recognition. The resulting data is then used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, place of registration, address, etc. The system is implemented and simulated in Matlab, and it performance is tested on real image. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.
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paper_title: Neuro semantic thresholding using OCR software for high precision OCR applications

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This paper describes a novel approach to binarization techniques. It presents a way of obtaining a threshold that depends both on the image and the final application using a semantic description of the histogram and a neural network. The intended applications of this technique are high precision OCR algorithms over a limited number of document types. The input image histogram is smoothed and its derivative is found. Using a polygonal version of the derivative and the smoothed histogram, a new description of the histogram is calculated. Using this description and a training set, a general neural network is capable of obtaining an optimum threshold for our application.
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paper_title: A Generalized Thinning Algorithm for Cursive and Non-Cursive Language Scripts

paper_content:

One of the most crucial phases in the process of text recognition is thinning of characters to a single pixel notation. The success measure of any thinning algorithm lies in its property to retain the original character shape, which are also called unit-width skeletons. No agreed universal thinning algorithm exists to produce character skeletons from different languages, which is a pre-process for all subsequent phases of character recognition such as segmentation, feature extraction, classification, etc. Written natural languages based on their intrinsic properties can be classified as cursive and non-cursive. Thinning algorithms when applied on cursive languages, poses greater complexity due to their distinct non-isolated boundaries and complex character shapes such as in Arabic, Sindhi, Urdu, etc. Such algorithms can easily be extended for parallel implementations. Selecting certain pixel arrangement grid templates over the other pixel patterns for the purpose of generating character skeletons exploits the parallel programming. The success key is in determining the right pixel arrangement grids that can reduce the cost of iterations required to evaluate each pixel for selecting for thinning or ignoring. This paper presents an improved parallel thinning algorithm, which can be easily extended for cursive or non-cursive languages alike by introducing a modified set of preservation rules via pixel arrangement grid templates, making it both robust to noise and speed. Experimental results show its success over cursive languages like Arabic, Sindhi, Urdu and non-cursive languages like English, Chinese and even numerals. Thus making it probably a universal thinning algorithm
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paper_title: Convolutional Neural Network Committees for Handwritten Character Classification

paper_content:

In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.
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paper_title: Convolutional Neural Network Committees for Handwritten Character Classification

paper_content:

In 2010, after many years of stagnation, the MNIST handwriting recognition benchmark record dropped from 0.40% error rate to 0.35%. Here we report 0.27% for a committee of seven deep CNNs trained on graphics cards, narrowing the gap to human performance. We also apply the same architecture to NIST SD 19, a more challenging dataset including lower and upper case letters. A committee of seven CNNs obtains the best results published so far for both NIST digits and NIST letters. The robustness of our method is verified by analyzing 78125 different 7-net committees.
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Title: A Survey on Optical Character Recognition System
Section 1: Literature Review
Description 1: Summarize the historical background and evolution of Optical Character Recognition (OCR) systems from early mechanical devices to modern advancements, including key milestones and research developments.

Section 2: Types of Optical Character Recognition Systems
Description 2: Discuss the various categories of OCR systems based on different criteria such as image acquisition mode, character connectivity, and font restrictions. Highlight the characteristics and challenges of different types.

Section 3: Major Phases of OCR
Description 3: Outline the primary steps involved in the OCR process, detailing each phase along with its significance and common techniques used.

Section 4: Image Acquisition
Description 4: Elaborate on the initial step of OCR which involves capturing and converting a digital image into a suitable format for processing. Discuss techniques like quantization and compression.

Section 5: Pre-processing
Description 5: Describe the methods used to enhance the quality of the captured image including thresholding, filtering, morphological operations, and skew estimation.

Section 6: Character Segmentation
Description 6: Explain the process of segmenting the image into individual characters, and how this step is crucial for effective classification.

Section 7: Feature Extraction
Description 7: Investigate the techniques used to extract distinctive features from segmented characters. Discuss the importance of selecting the right features and methods like principal component analysis.

Section 8: Classification
Description 8: Detail the methods used to classify characters into appropriate categories using structural and statistical approaches. Mention common classifiers such as Bayesian, neural networks, and decision trees.

Section 9: Post-processing
Description 9: Discuss techniques to enhance the accuracy of OCR results after initial classification, including the use of multiple classifiers, contextual analysis, and lexical processing.

Section 10: Conclusion
Description 10: Provide a summary of the paper, highlighting the importance of each phase in OCR, potential applications, current challenges especially in recognizing complex languages, and recommendations for future research directions.