Title :
A new HMM training and testing scheme
Author :
Ko, Albert Hung-Ren ; Sabourin, Robert ; de Souza Britto, A.
Author_Institution :
Univ. of Toronto, Toronto, ON, Canada
Abstract :
One of disadvantages of Hidden Markov Models (HMMs) is its low resistance to unexpected noises among observation sequences. Unexpected noises in a sequence usually ¿break¿ a sequence of observations, and then makes this sequence unrecognizable for trained models. We propose a new HMM training and testing scheme, which compensates some of the negative effects of such noises. We carried out experiment on handwritten digit recognition problem and the result suggests our proposal can be as effective as multi classifier systems.
Keywords :
handwritten character recognition; hidden Markov models; learning (artificial intelligence); pattern classification; HMM training; handwritten digit recognition problem; hidden Markov model; multiclassifier system; observation sequences; unexpected noises; Databases; Handwriting recognition; Hidden Markov models; Mathematical model; NIST; Pattern recognition; Proposals; Stochastic processes; Testing;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761250
