Campus Units

Statistics, Center for Statistics and Applications in Forensic Evidence (CSAFE)"

Document Type

Article

Publication Version

Published Version

Publication Date

11-24-2020

Journal or Book Title

Statistical Analysis and Data Mining: The ASA Data Science Journal

Volume

14

Issue

1

First Page

41

Last Page

60

DOI

10.1002/sam.11488

Abstract

Handwritten documents can be characterized by their content or by the shape of the written characters. We focus on the problem of comparing a person's handwriting to a document of unknown provenance using the shape of the writing, as is done in forensic applications. To do so, we first propose a method for processing scanned handwritten documents to decompose the writing into small graphical structures, often corresponding to letters. We then introduce a measure of distance between two such structures that is inspired by the graph edit distance, and a measure of center for a collection of the graphs. These measurements are the basis for an outlier tolerant K‐means algorithm to cluster the graphs based on structural attributes, thus creating a template for sorting new documents. Finally, we present a Bayesian hierarchical model to capture the propensity of a writer for producing graphs that are assigned to certain clusters. We illustrate the methods using documents from the Computer Vision Lab dataset. We show results of the identification task under the cluster assignments and compare to the same modeling, but with a less flexible grouping method that is not tolerant of incidental strokes or outliers.

Comments

This article is published as Crawford, Amy M., Nicholas S. Berry, and Alicia L. Carriquiry. "A clustering method for graphical handwriting components and statistical writership analysis." Statistical Analysis and Data Mining: The ASA Data Science Journal 14, no. 1 (2021): 41-60. Posted with permission of CSAFE.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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