Learning HMM Structure for On-Line Handwriting Modelization

Authors:
Henri Binsztok;Thierry Artieres
Affiliations:
Université Paris VI;Université Paris VI
Venue:
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Year:
2004

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A large margin approach for writer independent online handwriting classification

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Abstract

We present an Hidden Markov Model-based approach to model on-line handwriting sequences. This problem is addressed in term of learning both Hidden Markov Models(HMM) structure and parameters from data. We iteratively simplify an initial HMM that consists in a mixture of as many left-right HMM as training sequences. There are two main applications of our approach: allograph identification and classification. We provide experimental results on these two different tasks.