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Proceedings Paper

Recognition of Arabic handwritten words using contextual character models
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Paper Abstract

In this paper we present a system for the off-line recognition of cursive Arabic handwritten words. This system in an enhanced version of our reference system presented in [El-Hajj et al., 05] which is based on Hidden Markov Models (HMMs) and uses a sliding window approach. The enhanced version proposed here uses contextual character models. This approach is motivated by the fact that the set of Arabic characters includes a lot of ascending and descending strokes which overlap with one or two neighboring characters. Additional character models are constructed according to characters in their left or right neighborhood. Our experiments on images of the benchmark IFN/ENIT database of handwritten villages/towns names show that using contextual character models improves recognition. For a lexicon of 306 name classes, accuracy is increased by 0.6% in absolute value which corresponds to a 7.8% reduction in error rate.

Paper Details

Date Published: 28 January 2008
PDF: 9 pages
Proc. SPIE 6815, Document Recognition and Retrieval XV, 681503 (28 January 2008); doi: 10.1117/12.765868
Show Author Affiliations
Ramy El-Hajj, Univ. of Balamand (Lebanon)
Ecole Nationale Supérieure des Télécommunications (France)
Chafic Mokbel, Univ. of Balamand (Lebanon)
Laurence Likforman-Sulem, Ecole Nationale Supérieure des Télécommunications (France)


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

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