Skip to content

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

Conference/Workshop:
American Academy of Forensic Sciences Annual Scientific Meeting
Published: 2019
Primary Author: Amy Crawford
Secondary Authors: Alica L. Carriquiry, Danica Ommen
Research Area: Handwriting

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 these features overlap with features that questioned document examiners typically
examine.

Related Resources

A statistical approach to aid examiners in the forensic analysis of handwriting

A statistical approach to aid examiners in the forensic analysis of handwriting

We develop a statistical approach to model handwriting that accommodates all styles of writing (cursive, print, connected print). The goal is to compute a posterior probability of writership of a…
CSAFE Project Update & ASCLD FRC Collaboration

CSAFE Project Update & ASCLD FRC Collaboration

This presentation highlighted CSAFE’s collaboration with the ASCLD FRC Collaboration Hub.
Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data

Twin Convolutional Neural Networks to Classify Writers Using Handwriting Data

Identifying the source of handwriting is an important application in the field of forensic science that addresses questioned document evidence found in criminal cases and civil litigation. It is difficult,…
Quantifying Bayes Factors for Forensic Handwriting Evidence

Quantifying Bayes Factors for Forensic Handwriting Evidence

Questioned Document Examiners (QDEs) are tasked with analyzing handwriting evidence to make source (or writership) determinations. The Center for Statistics and Applications of Forensic Evidence (CSAFE) has previously developed computational…