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13 January 2003 Binary Vector Dissimilarity Measures for Handwriting Identification
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Proceedings Volume 5010, Document Recognition and Retrieval X; (2003) https://doi.org/10.1117/12.473347
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bin Zhang and Sargur N. Srihari "Binary Vector Dissimilarity Measures for Handwriting Identification", Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); https://doi.org/10.1117/12.473347
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Cited by 63 scholarly publications.
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KEYWORDS
Binary data

Forensic science

Distance measurement

Feature extraction

Pattern recognition

Computer engineering

Computer science

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