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Learning HMM structure for on-line handwriting modelization

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2 Author(s)
Binsztok, H. ; LIP6, Universite Paris IV, France ; Artieres, T.

We present a 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.

Published in:

Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Ninth International Workshop on

Date of Conference:

26-29 Oct. 2004

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