Title :
Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
Author :
Artieres, Thierry ; Marukatat, Sanparith ; Gallinari, Patrick
Author_Institution :
LIPS, Univ. Paris
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
Keywords :
gesture recognition; handwriting recognition; handwritten character recognition; hidden Markov models; image segmentation; learning (artificial intelligence); Asian characters; Latin characters; command gestures; geometric shapes; incremental learning; online handwritten shape recognition; segmental hidden Markov models; small drawings; symbols; Commercialization; Control systems; Graphics; Handwriting recognition; Hidden Markov models; Home appliances; Ink; Personal digital assistants; Prototypes; Shape; Two-dimensional shape recognition; gesture recognition; graphics recognition; online handwriting; pen-based interface; user-centric interface.; Algorithms; Artificial Intelligence; Automatic Data Processing; Handwriting; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Markov Chains; Models, Statistical; Online Systems; Pattern Recognition, Automated;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.38
