Arabic Handwriting Recognition Using Baseline Dependant Features and Hidden Markov Modeling
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Visual recognition of Arabic handwriting: challenges and new directions
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Human reading based strategies for off-line Arabic word recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Arabic bank check analysis and zone extraction
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Segmentation of Arabic Characters: A Comprehensive Survey
International Journal of Technology Diffusion
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A perfect segmentation method would be capable to segment words in letters. It would be then possible to define a process on letters. Unfortunately, such a method is almost impossible to obtain due to the nature of handwritten words. To tackle this problem, our approach segments the word into graphemes. We propose in this paper an analytical approach based on the Hidden Markovian Models (HMMs) to manage the defaults of the segmentation module. We also selected an optimal alphabet of graphemes in order to increase the performances of the recognition system. Furthermore, HMMs being developed exploit and model the notion of sub-words that is inherent to Arabic handwriting. An average correction of recognition rate of over 82.5% is obtained (in the first rank) with a lexicon of 232 different Tunisian state names.