Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/780
Title: Recognition of Arabic handwritten words using contextual character models
Authors: Hajj Mohamad, Ramy Al
Mokbel, Chafic 
Likforman-Sulem, Laurence
Affiliations: Department of Electrical Engineering 
Keywords: Arabic words
HMM
Contextual character model
AWHR
Handwriting recognition
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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