Binary segmentation with neural validation for cursive handwriting recognition

Authors:
Hong Lee;Brijesh Verma
Affiliations:
CQ University, Australia;CQ University, Australia
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Over-Segmentation and Validation (OSV) is a well anticipated segmentation strategy in cursive off-line handwriting recognition. Over-Segmentation is a means of locating all possible character boundaries, and the excessive segmentation points called over-segmentation points. Validation is a process to check and validate the segmentation points whether or not they are correct character boundaries by commonly employing an intelligent classifier trained with knowledge of characters. The existing OSV algorithms use ordered validation which means that the incorrect segmentation points might account for the validity of the next segmentation point. The ordered validation creates problems such as chain-failure. This paper presents a novel Binary Segmentation with Neural Validation (BSNV) to reduce the chain-failure. BSNV contains modules of over-segmentation and validation but the main distinctive feature of BSNV is an unordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are promising.