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Title: Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition
Authors: Bianne-Bernard, Anne-Laure
Menasri, Fares
Likforman-Sulem, Laurence
Mokbel, Chafic 
Affiliations: Department of Electrical Engineering 
Keywords: Arabic handwriting recognition
HMM-based system
State-based clustering
Issue Date: 2012
Part of: Proceedings of SPIE
Conference: Document Recognition and Retrieval Conference (19th : 24-26 Jan 2012 : San Francisco, CA, USA) 
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.
Open URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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