On-line handwritten shape recognition using segmental Hidden Markov Models

Abstract : We investigate a new approach for online handwritten shape recognition. Interesting features of this approach include learning without manual tuning, learning from very few training samples, incremental learning of characters, and adaptation to the user-specific needs. The proposed system can deal with two-dimensional graphical shapes such as Latin and Asian characters, command gestures, symbols, small drawings, and geometric shapes. It can be used as a building block for a series of recognition tasks with many applications.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01170742
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Submitted on : Thursday, July 2, 2015 - 11:49:42 AM
Last modification on : Thursday, September 19, 2019 - 2:20:04 PM

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Thierry Artières, Sanparith Marukatat, Patrick Gallinari. On-line handwritten shape recognition using segmental Hidden Markov Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2007, 29 (2), pp.205-217. ⟨10.1109/TPAMI.2007.38⟩. ⟨hal-01170742⟩

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