TY - GEN
T1 - Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition
AU - Bianne-Bernard, Anne Laure
AU - Menasri, Fares
AU - Likforman-Sulem, Laurence
AU - Mokbel, Chafic
AU - Kermorvant, Christopher
PY - 2012/2/27
Y1 - 2012/2/27
N2 - 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.
AB - 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.
KW - Arabic handwriting recognition
KW - HMM-based system
KW - state-based clustering
U2 - 10.1117/12.912093
DO - 10.1117/12.912093
M3 - Conference contribution
AN - SCOPUS:84857305908
SN - 9780819489449
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Document Recognition and Retrieval XIX
T2 - Document Recognition and Retrieval XIX
Y2 - 25 January 2012 through 26 January 2012
ER -