Word-Based Handwritten Arabic Scripts Recognition Using Dynamic Bayesian Network
نویسندگان
چکیده
In this paper, multi-class classification system is of handwritten Arabic words using Dynamic Bayesian Network (DBN) is proposed, in which technical details are presented in terms of three stages, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, features are extracted from each normalized word, where a set of new features for handwritten words is proposed based on a sliding window approach. The sliding window is moving across the mirrored word image. Finally, these features are then utilized to train a DBN for classification. The proposed system has been successfully tested on database (version v2.0p1e) consisting of 32492 Arabic words handwritten by more than 1000 different writers, and the results were promising and very encouraging. Keywords— Off-line handwritten recognition; feature extraction; dynamic Bayesian Network (DBN); IFN/ENIT database.
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