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Abstract:
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When forensic examiners compare handwritten evidence they often focus on small details of writing. Likewise, our statistical approach to the comparison of such documents begins by decomposing writing into small meaningful connected pieces of ink, often corresponding to letters. We treat these small pieces of connected ink as graphical structures with nodes and edges and group them into types based on similarity of their structures. The frequency at which graph types appear in writing, along with measurements taken on the small graphs serve as data for a Bayesian hierarchical mixture model. Through a latent variable, the model is able to separate print and cursive writings. The groupings are used to introduce a mixture aspect to the model and we assign different priors to similar groups of writers. We will present results of a writer identification analysis based on a closed set of writers.
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