Share Email Print

Proceedings Paper

Binary vector dissimilarity measures for handwriting identification
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top