Document image analysis for digital libraries
Proceedings of the 2006 international workshop on Research issues in digital libraries
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Modeling consistency of style in isogenous fields of patterns (such as character patterns in a word from the same font or writer) can improve classification accuracy. Since such patterns are interdependent, the Bayes classifier requires maximization of a probability score over all field-labels, which are exponentially more numerous with increasing field length. The iterative field classification algorithm prioritizes field-labels, for computation of probability scores, according to an upper bound on the score. Factorizability of the upper bound score allows dynamic prioritization of field-labels. Experiments on classification of numeral field patterns demonstrate computational efficiency of the algorithm.