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Confusion Model Selection Criterion for On-Line Handwritten Numeral Recognition  

Park, Mi-Na (강원대학교 컴퓨터정보통신공학과)
Ha, Jin-Young (강원대학교 컴퓨터학부)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.11, 2007 , pp. 1001-1010 More about this Journal
Abstract
HMM tends to output high probability for not only the proper class data but confusable class data, since the modeling power increases as the number of parameters increases. Thus it may not be helpful for discrimination to simply increase the number of parameters of HMM. We proposed two methods in this paper. One is a CMC(Confusion Likelihood Model Selection Criterion) using confusion class data probability, the other is a new recognition method, RCM(Recognition Using Confusion Models). In the proposed recognition method, confusion models are constructed using confusable class data, then confusion models are used to depress misrecognition by confusion likelihood is subtracted from the corresponding standard model probability. We found that CMC showed better results using fewer number of parameters compared with ML, ALC2, and BIC. RCM recorded 93.08% recognition rate, which is 1.5% higher result by reducing 17.4% of errors than using standard model only.
Keywords
HMM; BIC; CMC; ALC; Confustion; Topology optimization;
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