Combining different classification approaches to improve off-line Arabic handwritten word recognition

Ilya Zavorin; Eugene Borovikov; Ericson Davis; Anna Borovikov; Kristen Summers

doi:10.1117/12.767301

28 January 2008 Combining different classification approaches to improve off-line Arabic handwritten word recognition

Ilya Zavorin, Eugene Borovikov, Ericson Davis, Anna Borovikov, Kristen Summers

Proceedings Volume 6815, Document Recognition and Retrieval XV; 681504 (2008) https://doi.org/10.1117/12.767301
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Ilya Zavorin, Eugene Borovikov, Ericson Davis, Anna Borovikov, and Kristen Summers "Combining different classification approaches to improve off-line Arabic handwritten word recognition", Proc. SPIE 6815, Document Recognition and Retrieval XV, 681504 (28 January 2008); https://doi.org/10.1117/12.767301

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