“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 13363 |
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This paper concerns with the recognition of offline Farsi/Arabic handwriting. The overall appearance of each subword in Farsi/Arabic script is described by its shape contour that provides us with a rich set of discriminative characteristics. Our approach is writer-dependent; that is, the system is trained to recognize the subwords written by a particular writer. A fast contour alignment is the central part of the proposed algorithm, where the alignment is performed based on a handful of feature points. An efficient lexicon reduction algorithm based on characteristic loci feature, which works directly on subwords�?? binary images, is proposed as well. Fast and precise alignment along with efficient lexicon reduction and appropriate similarity matching yields a high recognition rate while kept the speed high. Our experiment on IBN SINA database shows that the correct classification rate could be as high as 91.08
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