13 January 2003 Binary Vector Dissimilarity Measures for Handwriting Identification
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Abstract
Several dissimilarity measures for binary vectors are formulated and examined for their recognition capability in handwriting identification for which the binary micro-features are used to characterize handwritten character shapes. Pertaining to eight dissimilarity measures, i.e., Jaccard-Needham, Dice, Correlation, Yule, Russell-Rao, Sokal-Michener, Rogers-Tanmoto and Kulzinsky, the discriminary power of ten individual characters and their combination is exhaustively studied. Conclusions are made on how to choose a dissimilarity measure and how to combine hybrid features.
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Bin Zhang, Bin Zhang, Sargur N. Srihari, Sargur N. Srihari, } "Binary Vector Dissimilarity Measures for Handwriting Identification", Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); doi: 10.1117/12.473347; https://doi.org/10.1117/12.473347
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