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, 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 in 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 behveen line and column features.
| Original language | English |
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| Pages (from-to) | 147-150 |
| Number of pages | 4 |
| Journal | Proceedings - International Conference on Pattern Recognition |
| Volume | 16 |
| Issue number | 3 |
| Publication status | Published - 1 Dec 2002 |