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This paper presents an off-line Arabic handwriting recognition system using the Hidden Markov Model Toolkit (HTK). HTK is a portable toolkit for speech recognition system. The recognition system extracts a set of features on binary handwritten images using sliding widow, builds character HMM models and learns word HMM models using embedded training without(More)
—In this paper, we present a method for lexicon size reduction which can be used as an important pre-processing for an off-line Arabic word recognition. The method involves extraction of the dot descriptors and PAWs (Piece of Arabic Word). Then the number and position of dots and the number of the PAWs are used to eliminate unlikely candidates. The(More)
Great challenges are faced in the offline recognition of cursive Arabic handwriting. This paper presents a segmentation-free system based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. The system first extracts a set of robust features on binary handwritten images by sliding windows.(More)
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