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Title: Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition
Authors: Bernard, Anne-Laure Bianne
Menasri, Fares
Hajj Mohamad, Ramy Al
Mokbel, Chafic 
Kermorvant, Christopher
Likforman-Sulem, Laurence
Affiliations: Department of Electrical Engineering 
Keywords: Hidden Markov models
Context modeling
Feature extraction
Handwriting recognition
Computational modeling
Issue Date: 2011
Part of: IEEE transactions on pattern analysis and machine intelligence
Volume: 33
Issue: 10
Start page: 2066
End page: 2080
This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. 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. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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