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23 January 2012 Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition
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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.
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Anne-Laure Bianne-Bernard, Fares Menasri, Laurence Likforman-Sulem, Chafic Mokbel, and Christopher Kermorvant "Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition", Proc. SPIE 8297, Document Recognition and Retrieval XIX, 829708 (23 January 2012); doi: 10.1117/12.912093;

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