Online handwritten shape recognition using segmental hidden Markov models.
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.
Artières
Thierry
T
LIP6, Université Paris, France. Thierry.artieres@lip6.fr
Marukatat
Sanparith
S
Gallinari
Patrick
P
D000465:Algorithms; D001185:Artificial Intelligence; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*