Feature relevance analysis for writer identification

Imran Siddiqi; Khurram Khurshid; Nicole Vincent

doi:10.1117/12.873309

24 January 2011 Feature relevance analysis for writer identification

Imran Siddiqi, Khurram Khurshid, Nicole Vincent

Proceedings Volume 7874, Document Recognition and Retrieval XVIII; 78740F (2011) https://doi.org/10.1117/12.873309
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

This work presents an analytical study on the relevance of features in an existing framework for writer identification from offline handwritten document images. The identification system comprises a set of 15 features combining the orientation and curvature information in a writing with the well-known codebook based approach. This study aims to find the optimal feature subset to identify the author of a questioned document while maintaining acceptable identification rates. Employing a genetic algorithm with a wrapper method we carry out a feature selection mechanism and identify the most relevant features that characterize the writer of a handwritten document.

Citation Download Citation

Imran Siddiqi, Khurram Khurshid, and Nicole Vincent "Feature relevance analysis for writer identification", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740F (24 January 2011); https://doi.org/10.1117/12.873309

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