In the field of forensic document examination, there are cases where comparing a questioned document to samples from a closed set of writers is adequate –– for example, a bomb threat note found in a school that must have been written by a current student. CSAFE handwriting research aims to extend this approach to more general cases by developing assessment methods that analyze the quality of handwriting images for use in automated comparison systems. Several automated systems capable of extracting features of writing for comparison between documents are available, however, they are expensive and their algorithms are proprietary. So, CSAFE developed an open-source program called handwriter that outputs glyphs, or geometric representations of handwriting. This key resource is available to the public, and supports advancements in CSAFE handwriting analysis research.
Iowa State University
Iowa State University
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CSAFE built a prototype model that uses glyph frequencies derived from a questioned document using the handwriter package to estimate the posterior probabilities of authorship from among a set of writers. Expanding and enhancing this model is a high priority. We are continuing to collect standard writing samples from a large set of writers to use in this research project. The data will be publicly available to support the efforts of other researchers.
Document examiners are often asked to determine the source of a handwritten document. The type of document to which we refer includes ransom notes, faked legal documents and other such documents, where the information about the source is contained in the handwriting itself rather than in the content of the document.
At present, document examiners rely on visual comparisons and subjective assessments of the similarity between two handwriting samples. They focus on attributes such as the width and length of loops, the crossing of t’s, the overall slant of the characters and such to determine whether a specific person might have been the author of the document.
Several years ago, a proprietary software called FLASH ID®  was introduced in the market. FLASH ID decomposes writing into graphical structures called graphemes, which are then clustered into a large number of deterministic groups depending on their morphology and other characteristics. From a large but closed set of reference documents, FLASH ID® selects those that are most like a question document using a set of quantitative criteria.
Researchers in CSAFE have produced a different program for the analysis of handwriting that is in the public domain. Handwriter, like FLASH ID®, decomposes writing into graphical structures called glyphs but uses a different set of rules to do so. Furthermore, glyphs in a document are allocated to 40 groups using a stochastic clustering algorithm called k-means, which appears to be more robust than a deterministic grouping approach. Also with a closed set of writers (that can be very large), the frequency with which a writer contributes glyphs to each group, and the attributes of the glyphs in the clusters are then used to compute the probability that anyone of the writers in the reference set may have authored the questioned document.
Initial results are promising; using a set of 90 writers recruited nationwide to participate in a study organized by CSAFE, we were able to correctly establish authorship of a questioned document with about 98% accuracy. At present, authorship is established if the posterior probability of writership is 0.9 or higher.
While there are many cases in which comparing a questioned document to samples from a closed set of writers is adequate (e.g., a bomb threat note found in a school that must have been written by an unknown student in the school), extending this approach to the more general case is an objective of the work we propose here. We also aim to explore the effect of image quality on the results of the writership determinations based on the data provided by handwriter.
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Published: 2023 | By: Amy Crawford
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 questioned document given a closed set of candidate writers. Such…
Published: 2022 | By: Jeff Salyards
This presentation highlighted CSAFE's collaboration with the ASCLD FRC Collaboration Hub.
Published: 2023 | By: Pilhyun (Andrew) Lim
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, given the idiosyncrasies of a person’s handwriting, to recognize the…
Published: 2023 | By: Anyesha Ray
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 methods to automatically extract quantifiable handwriting features and statistical methods…
Published: 2023 | By: Alexandria Arabio
Handwriting comparative analysis is based on the principle that no two individuals can produce the same writing and that an individual cannot exactly reproduce his/her handwriting. This project aims to assess and quantify the natural variations produced by a distinct…
An Overview of the Two-Stage, Score-Based Likelihood Ratio, and Bayes Factor Approaches for Writership Determinations
Published: 2023 | By: Danica Ommen
A variety of statistical approaches have been developed at the Center for Statistics and Applications in Forensic Evidence (CSAFE) to address the question of writership for forensic document examinations. Previous work at CSAFE has addressed the closed-set problem, when the…
A Rotation-Based Feature and Bayesian Hierarchical Model for the Forensic Evaluation of Handwriting Evidence in a Closed Set
Published: 2023 | By: Amy Crawford
Forensic handwriting examiners are often tasked with identifying the writer of a particular document. Examples of handwriting evidence include ransom notes, forged documents and signatures, and threatening letters. At present, examiners rely on visual inspection of similarities and differences between…
Published: 2022 | By: Stephanie Reinders
FLASH ID and handwriter are computer programs that compare questioned handwritten documents against handwritten samples from known writers. FLASH ID was developed by Sciometrics and is used by the FBI, Handwriter is an open-source R package designed by CSAFE. We…
Published: 2022 | By: Danica Ommen
Presentation is from the 106th International Association for Identification (IAI) Annual Educational Conference
Published: 2022 | By: Andrew Lim
Primary goals are to examine: 1. Write diversification versus representation. 2. Preservation of handwriting structure versus image density. 3. Input size versus training size. 4. Writer identification complexity assessment using various test sites.
Published: 2022 | By: Alicia Carriquiry
The information below highlights a sample of current research initiatives led by the CSAFE team. Additional accomplishments in other forensic science disciplines will be discussed in subsequent issues of Forensic Science Review. Visit the CSAFE website www.forensicstats.org to learn more…
Published: 2022 | By: Dr. Alicia Carriquiry
This CSAFE webinar was held on October 18, 2022. Presenters: Alicia Carriquiry Director, CSAFE Distinguished Professor and President’s Chair, Department of Statistics – Iowa State University Stephanie Reinders Research Scientist, CSAFE Presentation Description: Forensic handwriting analysis relies on the principle…
Published: 2021 | By: Danica Ommen
Handwriting analysis is conducted through the expertise of Forensic Document Examiners (FDEs) by visually comparing writing samples. Through their training and years of experience, FDEs are able to recognize critical characteristics of writing to evaluate the evidence of writership. In…
Published: 2021 | By: Madeline Johnson
Handwriting analysis is conducted by forensic document examiners who are able to visually recognize characteristics of writing to evaluate the evidence of writership. Recently, there have been incentives to investigate how to quantify the similarity between two written documents to…
The 2021 Field Update was held June 14, 2021, and served as the closing to the first year of CSAFE 2.0. CSAFE brought together researchers, forensic science partners and interested community members to highlight the organization’s achievements, identify areas for…
This CSAFE webinar was held on March 11, 2021. Presenter: Alicia Carriquiry Distinguished Professor and President's Chair in Statistics, CSAFE Director Presentation Description: Forensic handwriting analysis relies on the principle of individuality: no two writers produce identical writing, and given…
Published: 2020 | By: Amy M. Crawford
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…
Published: 2020 | By: Amy Crawford
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…
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 new initiatives for the next phase of the center, CSAFE…
An Exploratory Analysis of Handwriting Features: Investigating Numeric Measurements of Writing That Are Important for Statistical Modeling
Published: 2019 | By: Amy Crawford
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…
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