A comparative study between decision fusion and data fusion in Markovian printed character recognition
Khalid Hallouli, Laurence Likforman-Sulem, Marc Sigelle · 2003
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.