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Proceedings Paper

Combining different classification approaches to improve off-line Arabic handwritten word recognition
Author(s): Ilya Zavorin; Eugene Borovikov; Ericson Davis; Anna Borovikov; Kristen Summers
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Paper Abstract

Machine perception and recognition of handwritten text in any language is a difficult problem. Even for Latin script most solutions are restricted to specific domains like bank checks courtesy amount recognition. Arabic script presents additional challenges for handwriting recognition systems due to its highly connected nature, numerous forms of each letter, and other factors. In this paper we address the problem of offline Arabic handwriting recognition of pre-segmented words. Rather than focusing on a single classification approach and trying to perfect it, we propose to combine heterogeneous classification methodologies. We evaluate our system on the IFN/ENIT corpus of Tunisian village and town names and demonstrate that the combined approach yields results that are better than those of the individual classifiers.

Paper Details

Date Published: 28 January 2008
PDF: 11 pages
Proc. SPIE 6815, Document Recognition and Retrieval XV, 681504 (28 January 2008); doi: 10.1117/12.767301
Show Author Affiliations
Ilya Zavorin, CACI International Inc. (United States)
Eugene Borovikov, CACI International Inc. (United States)
Ericson Davis, CACI International Inc. (United States)
Anna Borovikov, CACI International Inc. (United States)
Kristen Summers, CACI International Inc. (United States)

Published in SPIE Proceedings Vol. 6815:
Document Recognition and Retrieval XV
Berrin A. Yanikoglu; Kathrin Berkner, Editor(s)

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