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Proceedings Paper

Feature relevance analysis for writer identification
Author(s): Imran Siddiqi; Khurram Khurshid; Nicole Vincent
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Paper 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.

Paper Details

Date Published: 24 January 2011
PDF: 9 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740F (24 January 2011); doi: 10.1117/12.873309
Show Author Affiliations
Imran Siddiqi, LIAPDE-SIP, Paris Descartes Univ. (France)
National Univ. of Sciences and Technology (Pakistan)
Khurram Khurshid, LIAPDE-SIP, Paris Descartes Univ. (France)
Institute of Space Technology (Pakistan)
Nicole Vincent, LIAPDE-SIP, Paris Descartes Univ. (France)

Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)

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