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19 January 2009 Combination of dynamic Bayesian network classifiers for the recognition of degraded characters
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Abstract
We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.
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Laurence Likforman-Sulem and Marc Sigelle "Combination of dynamic Bayesian network classifiers for the recognition of degraded characters", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470H (19 January 2009); doi: 10.1117/12.805471; https://doi.org/10.1117/12.805471
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