Comparison of statistical models for writer verification

Sargur Srihari; Gregory R. Ball

doi:10.1117/12.806077

19 January 2009 Comparison of statistical models for writer verification

Sargur Srihari, Gregory R. Ball

Proceedings Volume 7247, Document Recognition and Retrieval XVI; 72470E (2009) https://doi.org/10.1117/12.806077
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

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.

Citation Download Citation

Sargur Srihari and Gregory R. Ball "Comparison of statistical models for writer verification", Proc. SPIE 7247, Document Recognition and Retrieval XVI, 72470E (19 January 2009); https://doi.org/10.1117/12.806077

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