Abstract:
A comparison is made between several hidden Markov models in the context of printed character recognition. Two HMMs are first compared, one dealing with columns of a char...Show MoreMetadata
Abstract:
A comparison is made between several hidden Markov models in the context of printed character recognition. Two HMMs are first compared, one dealing with columns of a character image, and the other dealing with lines. These 2 HMMs are then associated in a decision fusion scheme combining the log-likelihoods provided by each HMM classifier. The statistical assumptions underlying the combination formula are described and the combination formula is shown to be an approximation of a real joint log-likelihood. The last experiment consists of building a single HMM, modeling the joint flow of lines and columns. This data fusion scheme is shown to be more accurate as it highlights correlations between the line and column features.
Published in: 2002 International Conference on Pattern Recognition
Date of Conference: 11-15 August 2002
Date Added to IEEE Xplore: 10 December 2002
Print ISBN:0-7695-1695-X
Print ISSN: 1051-4651