Over-Segmentation and Neural Binary Validation for Cursive Handwriting Recognition

A novel Over-Segmentation and Neural Binary Validation (OSNBV) is presented in this paper. OSNBV is a character segmentation strategy for off-line cursive handwriting recognition. Unlike the approaches in the literature, OSNBV is a prioritized segmentation approach. Initially, OSNBV over-segments a handwritten word into primitives. Neural binary validation is iteratively applied to the primitives. The outcome of each iteration is to join two neighboring primitives when the joined one improves the global neural competency. OSNBV introduces Transition Count (TC) and TC for English (EngTC) to prevent under-segmentation error during neural binary validation. OSNBV also incorporates Transition Count Matrix (TCM) into neural global competency. The proposed approach has been evaluated on CEDAR benchmark database. The results showed a significant improvement in segmentation errors. The analysis of results showed that the inclusion of TCM into the validation function has played a major role in improving over-segmentation and bad-segmentation errors.