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Proceedings Paper

Binary vector dissimilarity measures for handwriting identification
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Paper 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.

Paper Details

Date Published: 13 January 2003
PDF: 11 pages
Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); doi: 10.1117/12.473347
Show Author Affiliations
Bin Zhang, Univ. at Buffalo (United States)
Sargur N. Srihari, Univ. at Buffalo (United States)


Published in SPIE Proceedings Vol. 5010:
Document Recognition and Retrieval X
Tapas Kanungo; Elisa H. Barney Smith; Jianying Hu; Paul B. Kantor, Editor(s)

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