Coupling observation/letter for a Markovian modelisation applied to the recognition of Arabic handwriting
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.