Binary vector dissimilarity measures for handwriting identification

Bin Zhang; Sargur N. Srihari

doi:10.1117/12.473347

Translator Disclaimer

13 January 2003 Binary Vector Dissimilarity Measures for Handwriting Identification

Bin Zhang, Sargur N. Srihari

Author Affiliations +

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE