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Binary segmentation with neural validation for cursive handwriting recognition

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conference contribution
posted on 06.12.2017, 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.

History

Start Page

1730

End Page

1735

Number of Pages

6

Start Date

01/01/2009

eISSN

1098-7576

ISBN-13

9781424435531

Location

Atlanta, Georgia, USA

Publisher

IEEE

Place of Publication

NJ, USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Arts, Business, Informatics and Education; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Name of Conference

IEEE International Joint Conference on Neural Networks