A vectorizer and feature extractor for document recognition
Computer Vision, Graphics, and Image Processing
A Method of Recognition of Arabic Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine recognition of printed Arabic text utilizing natural language morphology
International Journal of Man-Machine Studies
A method of coding hand-written Arabic characters and its application to context-free grammar
Pattern Recognition Letters
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Handwritten Cursive Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Baseline Estimation For Arabic Handwritten Words
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
A class-modular GLVQ ensemble with outlier learning for handwritten digit recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT- Database
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
A multiexpert framework for character recognition: a novel application of Clifford networks
IEEE Transactions on Neural Networks
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Segmentation of the handwritten Arabic characters is still one of the most difficult problems to develop a reliable Arabic OCR. This paper presents a complete Arabic OCR system that uses histogram clustering method for the segmentation of the Arabic word. This method gives the ability to process different user styles, and manages the variability of pen strokes. Also, a new algorithm for separating overlapped characters was proposed to support the proposed technique for segmentation. The feature extraction process was based on a combination between the PCA network and characters geometric features. A classifier for hundred of Arabic character images was designed using a decision tree induction algorithm, and MLP network. A segmentation correctness of 96% was achieved while the recognition rate of the whole system was 91.5%.