Issue No. 01 - January (1999 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.745738
<p><b>Abstract</b>—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.</p>
Lexicon-driven, handwritten word recognition, linear combination of order statistics, dynamic programming, normalized edit distance, fuzzy integrals.
H. Shi, P. Gader and W. Chen, "Lexicon-Driven Handwritten Word Recognition Using Optimal Linear Combinations of Order Statistics," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 77-82, 1999.