Lexicon-driven handwritten word recognition using optimal linear combinations of order statistics
In the standard segmentation-based approach to handwritten word recognition, individual character-class confidence scores are combined via averaging to estimate confidences in the hypothesized identities for a word. We describe a methodology for generating optimal Linear Combination of Order Statistics operators for combining character class confidence scores. Experimental results are provided on over 1,000 word images.
lexicon-driven handwritten word recognition linear combination of order statistics dynamic programming normalized edit distance fuzzy integrals