A multi-level perception approach to reading cursive script
Artificial Intelligence
The State of the Art in Online Handwriting Recognition
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 Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Arabic Cursive Script
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Confidence Measures for an Address Reading System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Multiagents to Separating Handwritten Connected Digits
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Image Processing
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The segmentation of words into characters is a main stage in character recognition systems. In this paper, a novel approach is proposed to segment Arabic words, written in Naskh handwriting style. The segmentation algorithm is based on seven agents which cooperate to detect regions where segmentation is illegal. Then, end point features are extracted from the remaining regions of the word and the middle of every two successive end points is considered as a candidate segmentation point if specific rules are satisfied. The experimental results are very promising and achieve a success rate of 86%