Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a method of classifying a pattern using information furnished by a ranked list of templates, rather than just the best matching template. We propose a parsimonious model to compute the class-conditional likelihood of a list of templates ranked on the basis of their match scores. We discuss the estimation of parameters used in the model. The results of maximum likelihood classification on isolated digit patterns consistently show a 10-20% relative gain in recognition accuracy when we use more than one top-template.