Skip to content

Bayesian hierarchical modeling for the forensic evaluation of handwritten documents

Published: 2020
Primary Author: Amy Crawford
Research Area: Handwriting

The analysis of handwritten evidence has been used widely in courts in the United States since the 1930s (Osborn, 1946). Traditional evaluations are conducted by trained forensic examiners. More recently, there has been a movement toward objective and probability-based evaluation of evidence, and a variety of governing bodies have made explicit calls for research to support the scientific underpinnings of the field (National Research Council, 2009; President’s Council of Advisors on Science and Technology (US), 2016; National Institutes of Standards and Technology). This body of work makes contributions to help satisfy those needs for the evaluation of handwritten documents.

We develop a framework to evaluate a questioned writing sample against a finite set of genuine writing samples from known sources. Our approach is fully automated, reducing the opportunity for cognitive biases to enter the analysis pipeline through regular examiner intervention. Our methods are able to handle all writing styles together, and result in estimated probabilities of writership based on parametric modeling. We contribute open-source datasets, code, and algorithms.

A document is prepared for the evaluation processed by first being scanned and stored as an image file. The image is processed and the text within is decomposed into a sequence of disjoint graphical structures. The graphs serve as the smallest unit of writing we will consider, and features extracted from them are used as data for modeling. Chapter 2 describes the image processing steps and introduces a distance measure for the graphs. The distance measure is used in a K-means clustering algorithm (Forgy, 1965; Lloyd, 1982; Gan and Ng, 2017), which results in a clustering template with 40 exemplar structures. The primary feature we extract from each graph is a cluster assignment. We do so by comparing each graph to the template and making assignments based on the exemplar to which each graph is most similar in structure. The cluster assignment feature is used for a writer identification exercise using a Bayesian hierarchical model on a small set of 27 writers. In Chapter 3 we incorporate new data sources and a larger number of writers in the clustering algorithm to produce an updated template. A mixture component is added to the hierarchical model and we explore the relationship between a writer’s estimated mixing parameter and their writing style. In Chapter 4 we expand the hierarchical model to include other graph-based features, in addition to cluster assignments. We incorporate an angular feature with support on the polar coordinate system into the hierarchical modeling framework using a circular probability density function. The new model is applied and tested in three applications.

Related Resources

CSAFE 2020 All Hands Meeting

CSAFE 2020 All Hands Meeting

The 2020 All Hands Meeting was held May 12 and 13, 2020 and served as the closing to the last 5 years of CSAFE research and focused on kicking off…
An Exploratory Analysis of Handwriting Features: Investigating Numeric Measurements of Writing That Are Important for Statistical Modeling

An Exploratory Analysis of Handwriting Features: Investigating Numeric Measurements of Writing That Are Important for Statistical Modeling

The goal of this presentation is to provide insights into which features of handwritten documents are important for statistical modeling with the task of writer identification and to discuss how…
Statistical Analysis of Handwriting: Probabilistic Outcomes for Closed-Set Writer Identification

Statistical Analysis of Handwriting: Probabilistic Outcomes for Closed-Set Writer Identification

The goal of this presentation is to provide insights into features of handwritten documents that are important for statistical modeling with the task of writer identification.
A database of handwriting samples for applications in forensic statistics

A database of handwriting samples for applications in forensic statistics

Handwriting samples were collected from 90 adults for the purpose of developing statistical approaches to the evaluation of handwriting as forensic evidence. Each participant completed three data collection sessions, each…