Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking
Pattern Recognition Letters
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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This article describes an off-line handwritten Arabic words recognition system. Both explicit grapheme segmen- tation and feature extraction are originally designed for Latin cursive handwriting. The recognizer itself is a Hybrid HMM/NN. We introduce a new shape-based alphabet for handwriting Arabic recognition which is intended to benefit from some specificities of Arabic writing. We performed several experiments using IFN/ENIT benchmark database to validate our approach. Our rec- ognizer performs as close as the state of the art recogni- tion rate with 87%. The latter results are indeed very en- couraging as many perspectives and improvements may be considered. Especially, the explicit processing of dots and diacritics, therefore making use of more prior knowledge of Arabic writing specificities.