Binary segmentation with neural validation for cursive handwriting recognition
conference contributionposted on 2017-12-06, 00:00 authored by Hong Suk LeeHong Suk Lee, Brijesh Verma
Over-Segmentation and Validation (OSV) is a well anticipated segmentation strategy in cursive off-line hand writing 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 un-ordered segmentation strategy. The proposed algorithm has been evaluated on CEDAR benchmark database and the results of the experiments are promising.
Number of Pages6
LocationAtlanta, Georgia, USA
Place of PublicationNJ, USA
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External Author AffiliationsFaculty of Arts, Business, Informatics and Education; Institute for Resource Industries and Sustainability (IRIS);