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Conference papers

Learning HMM Structure for On-line Handwriting Modelization

Henri Binsztok 1 Thierry Artières 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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.
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Submitted on : Wednesday, May 10, 2017 - 3:14:05 PM
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Henri Binsztok, Thierry Artières. Learning HMM Structure for On-line Handwriting Modelization. 9th IAPR International Workshop on Frontiers in Handwriting Recognition, Oct 2004, Tokyo, Japan. pp.407-412, ⟨10.1109/IWFHR.2004.60⟩. ⟨hal-01520496⟩



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