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|Title:||Recognition of Arabic handwritten words using contextual character models||Authors:||Hajj Mohamad, Ramy Al
|Affiliations:||Department of Electrical Engineering||Keywords:||Arabic words
Contextual character model
|Issue Date:||2008||Part of:||Proceedings of SPIE - The International Society for Optical Engineering||Conference:||Document Recognition and Retrieval Conference (15th : 29-31 Jan 2008 : San Jose, CA, USA)||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.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/780||DOI:||10.1016/j.jesit.2017.02.001||Ezproxy URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Electrical Engineering|
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