A fast and accurate contour-based method for writer-dependent offline handwritten Farsi/Arabic subwords recognition

摘要

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%. This figure is achieved merely by subword shape matching, without dots and diacritics, and without any statistical language model.