Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence2007Vol. 29(2), pp. 205–217
Citations Over TimeTop 10% of 2007 papers
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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