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Proceedings Paper

Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition
Author(s): Anne-Laure Bianne-Bernard; Fares Menasri; Laurence Likforman-Sulem; Chafic Mokbel; Christopher Kermorvant
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Paper Abstract

We present in this paper an HMM-based recognizer for the recognition of unconstrained Arabic handwritten words. The recognizer is a context-dependent HMM which considers variable topology and contextual information for a better modeling of writing units. We propose an algorithm to adapt the topology of each HMM to the character to be modeled. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced which significantly reduces the number of parameters. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions yielding larger clusters and precise questions yielding smaller ones. We apply this modeling to the recognition of Arabic handwritten words. Experiments conducted on the OpenHaRT2010 database show that variable length topology and contextual information significantly improves the recognition rate.

Paper Details

Date Published: 23 January 2012
PDF: 8 pages
Proc. SPIE 8297, Document Recognition and Retrieval XIX, 829708 (23 January 2012); doi: 10.1117/12.912093
Show Author Affiliations
Anne-Laure Bianne-Bernard, A2iA SA (France)
Lab. Traitement et Communication de l'Information, CNRS, Telecom ParisTech (France)
Fares Menasri, A2iA SA (France)
Laurence Likforman-Sulem, Lab. Traitement et Communication de l'Information, CNRS, Telecom ParisTech (France)
Chafic Mokbel, Univ. of Balamand (Lebanon)
Christopher Kermorvant, A2iA SA (France)

Published in SPIE Proceedings Vol. 8297:
Document Recognition and Retrieval XIX
Christian Viard-Gaudin; Richard Zanibbi, Editor(s)

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