Recognition of off-line handwritten Arabic words using hidden Markov model approach

Hidden Markov models (HMM) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for totally unconstrained Arabic handwritten word recognition based on a model discriminant HMM is presented. A complete system able to classify Arabic handwritten words of one hundred different writers is proposed and discussed. The system first attempts to remove some of variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word so that feature information about the lines in the skeleton is extracted. Then a classification process based on the HMM approach is used. The output is a word in the dictionary. A detailed experiment is carried out and successful recognition results are reported.

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