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Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
February 2007 (vol. 29 no. 2)
pp. 205-217
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.
Index Terms:
Two-dimensional shape recognition, online handwriting, gesture recognition, graphics recognition, pen-based interface, user-centric interface.
Citation:
Thierry Arti?res, Sanparith Marukatat, Patrick Gallinari, "Online Handwritten Shape Recognition Using Segmental Hidden Markov Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 2, pp. 205-217, Feb. 2007, doi:10.1109/TPAMI.2007.38
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