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19 January 2009 Comparison of statistical models for writer verification
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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