Comparison of statistical models for writer verification

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.