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Proceedings Paper

Comparison of statistical models for writer verification
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Paper Abstract

A novel statistical model for determining whether a pair of documents, a known and a questioned, were written by the same individual is proposed. The goal of this formulation is to learn the specific uniqueness of style in a particular author's writing, given the known document. Since there are often insufficient samples to extrapolate a generalized model of an writer's handwriting based solely on the document, we instead generalize over the differences between the author and a large population of known different writers. This is in contrast to an earlier model proposed whereby probability distributions were a priori without learning. We show the performance of the model along with a comparison in performance to the non-learning, older model, which shows significant improvement.

Paper Details

Date Published: 19 January 2009
PDF: 8 pages
Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470E (19 January 2009); doi: 10.1117/12.806077
Show Author Affiliations
Sargur Srihari, Univ. at Buffalo (United States)
Gregory R. Ball, Univ. at Buffalo (United States)

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

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