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Proceedings Paper

Online writer identification using alphabetic information clustering
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Paper Abstract

Writer identification is a topic of much renewed interest today because of its importance in applications such as writer adaptation, routing of documents and forensic document analysis. Various algorithms have been proposed to handle such tasks. Of particular interests are the approaches that use allographic features [1-3] to perform a comparison of the documents in question. The allographic features are used to define prototypes that model the unique handwriting styles of the individual writers. This paper investigates a novel perspective that takes alphabetic information into consideration when the allographic features are clustered into prototypes at the character level. We hypothesize that alphabetic information provides additional clues which help in the clustering of allographic prototypes. An alphabet information coefficient (AIC) has been introduced in our study and the effect of this coefficient is presented. Our experiments showed an increase of writer identification accuracy from 66.0% to 87.0% when alphabetic information was used in conjunction with allographic features on a database of 200 reference writers.

Paper Details

Date Published: 19 January 2009
PDF: 8 pages
Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470F (19 January 2009); doi: 10.1117/12.805644
Show Author Affiliations
Guo Xian Tan, Nanyang Technological Univ. (Singapore)
IRCCyN, CNRS, Univ. de Nantes (France)
Christian Viard-Gaudin, IRCCyN, CNRS, Univ. de Nantes (France)
Alex C. Kot, Nanyang Technological Univ. (Singapore)

Published in SPIE Proceedings Vol. 7247:
Document Recognition and Retrieval XVI
Kathrin Berkner; Laurence Likforman-Sulem, Editor(s)

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