Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challenges. This paper provides a survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten. Also, it examines the literature on the most significant work in handwritten text recognition without segmentation and discusses algorithms which split the words into characters.

A Survey on Arabic Character Recognition

Concerning to optical character recognition, handwriting has sustained to persist as a means of communication and recording information in day to day life even with the introduction of new technologies. Hidden Markov Models (HMM) have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. A system for offline recognition of cursive handwritten Tamil characters is presented. In this effort, offline cursive handwritten recognition system for Tamil based on HMM and uses a combination of Time domain and frequency domain feature is proposed. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.

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Off-line Cursive Handwritten Tamil Character Recognition

In this paper, an Arabic letter recognition system based on Artificial Neural Networks (ANNs) and statistical analysis for feature extraction is presented. The ANN is trained using the Least Mean Squares (LMS) algorithm. In the proposed system, each typed Arabic letter is represented by a matrix of binary numbers that are used as input to a simple feature extraction system whose output, in addition to the input matrix, are fed to an ANN. Simulation results are provided and show that the proposed system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. Keywords—ANN, Backpropagation, Gaussian, LMS, MSE, Neuron, standard deviation, Widrow-Hoff rule.

Arabic Character Recognition using Artificial Neural Networks and Statistical Analysis

In this paper, an online isolated Arabic handwritten character recognition system is introduced. The system can be adapted to achieve the demands of hand$held and dig ital tablet applications. To achieve this goal, despite of single neural networks, four neural networks are used, one for each cluster of characters. Feed forward back propagation neural networks are used in classification process. This approach is employed as classifiers due to the low computation overhead during training and recall process. The system recognizes on$line isolated Arabic character and achieves an a ccuracy rate 9٥.7% from untrained writers and 99.1% for trained writers.

OIAHCR: Online Isolated Arabic Handwritten Character Recognition Using Neural Network

In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of challenges especially in recognizing cursive handwritten scripts of different languages. The concept proposed is a solution crafted to perform character recognition of hand-written scripts in Tamil, a language having official status in India, Sri Lanka, and Singapore. The approach utilizes discrete Hidden Markov Models (HMMs) for recognizing off-line cursive handwritten Tamil characters. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database and the precision of the results demonstrates its application on commercial usage. The methodology promises to present a simple and fast scaffold to construct a full OCR system extended with suitable pre-processing.

Off-line cursive handwritten Tamil character recognition

The objective of this study was to present a new technique assists in developing a recognition system for handling the Arabic Hand Written text. The proposed system is called Arabic Hand Written Optical Character Recognition (AHOCR). AHOCR was concerned with recognition of hand written Alphanumeric Arabic characters. In the present work, extracted characters are represented by using geometric moment invariant of order three. The advantage of using moment invariant for pattern classification as compared to the other methods is its invariant with respect to its: position , size and rotation .The proposed technique was divided into three major steps : the first step was concerned with digitization and preprocessing documents to create connect components, detect the skew of characters and correct it .The second step deals with how to use geometric moment invariant features of the input Arabic characters to extract features . The third step focused on description of an advanced system of classification using Probabilistic Neural networks structure which yields significant speed improvement. Our final results indicate and clarify that the proposed AHOCR technique achieves an excellent test accuracy of recognition rated up to 97% for isolated Arabic characters and 96% for Arabic text.

Basem Al Rifai

Papers

Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters