Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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Neural network (NN) have been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a Neural network is proposed and discussed. The overall engine of this combination of a global feature scheme with a NN, is a system able to classify Arabic-Handwritten words of one hundred different writers. The system first attempts to remove some of the 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 strokes in the skeleton is extracted. Then, a classification process based on the artificial NN classifier is used as global recognition engine, to classify the Arabic words. The output is a word in the dictionary. A detailed experiment is carried out, and successful recognition results are reported.