This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
Learning HMM Structure for On-Line Handwriting Modelization
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
Henri Binsztok, Universit? Paris VI
Thierry Arti?res, Universit? Paris VI
We present an Hidden Markov Model-based approach to model on-line handwriting sequences. This problem is addressed in term of learning both Hidden Markov Models(HMM) structure and parameters from data. We iteratively simplify an initial HMM that consists in a mixture of as many left-right HMM as training sequences. There are two main applications of our approach: allograph identification and classification. We provide experimental results on these two different tasks.
Index Terms:
HMM Structure Learning, Allograph Clustering
Citation:
Henri Binsztok, Thierry Arti?res, "Learning HMM Structure for On-Line Handwriting Modelization," iwfhr, pp.407-412, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
Usage of this product signifies your acceptance of the Terms of Use.