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 ...
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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 ...
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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.
Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that parti...
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