Segmentation of Offline Handwritten Bengali Script

Basu, Subhadip, Chaudhuri, Chitrita, Kundu, Mahantapas, Nasipuri, Mita, Basu, Dipak K.

arXiv.org Artificial Intelligence 

Character segmentation is one of the most important decision processes for optical character recognition (OCR). Isolating individual alphabetic characters in the script image is often significant enough to make a decisive contribution towards the success rate of the overall system. An OCR system may be designed to work for either of online and off-line purposes. Online OCR systems collect input data by recording the order of strokes made by the write on an electronic bit-pad, and off-line OCR systems do the same by recording the pixel by pixel digital image of the entire writing with a digital scanner. OCR has a wide field of application covering handwritten document transcription, automatic mail address recognition, machine processing of bankchecks, faxes etc. Off-line OCR of hand written words has long been an active area research. Some important contributions so far made in this field involve analysis of English texts [1], [2], [3], [5], Chinese script [6] and Arabic characters [9]. With this background of research, the present work considers Bengali script for developing suitable techniques for off-line OCR with it.

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