A Method of Recognition of Arabic Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer recognition of Arabic cursive scripts
Pattern Recognition
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two template matching approaches to Arabic, Amharic and Latin isolated characters recognition
Machine Graphics & Vision International Journal
Fast Zernike wavelet moments for Farsi character recognition
Image and Vision Computing
Expert Systems with Applications: An International Journal
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
Accurate tool based on JPEG image compression for Arabic handwritten character shape recognition
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Online handwriting recognition for the Arabic letter set
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
Offline handwritten arabic character segmentation with probabilistic model
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Automated system for Arabic optical character recognition
Proceedings of the 3rd International Conference on Information and Communication Systems
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A statistical approach for the recognition of Arabic characters is introduced. As a first step, the character is segmented into primary and secondary parts (dots and zigzags). The secondary parts of the character are then isolated and identified separately, thereby reducing the number of classes from 28 to 18. The moments of the horizontal and vertical projections of the remaining primary characters are then calculated and normalized with respect to the zero-order moment. Simple measures of the shape are obtained from the normalized moments. A 9-D feature vector is obtained for each character. Classification is accomplished using quadratic discriminant functions. The approach was evaluated using isolated, handwritten, and printed characters from a database established for this purpose. The results indicate that the technique offers better classification rates in comparison with existing methods.