Online Handwritten Shape Recognition Using Segmental Hidden Markov Models

Author

Artieres, Thierry ; Marukatat, Sanparith ; Gallinari, Patrick

Author_Institution

LIPS, Univ. Paris

Volume

29

Issue

2

fYear

2007

Firstpage

205

Lastpage

217

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;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2007.38

Filename

4042697