Handwriting proven
to be unique
By ELLEN
GOLDBAUM
Contributing Editor
Computer
scientists at UB have provided the first peer-reviewed scientific validation
that each person's handwriting is individual, according to a paper that
will be published in the Journal of Forensic Sciences in July.
The
UB research was cited in an April 29 decision of the U.S. District Court
for the Eastern District of Pennsylvania. That decision allows expert
testimony concerning handwritten documents pertinent to the case (U.S.
v. Gricco) into court, and it is one of the first recent court decisions
to do so.
Supported
by a National Institute of Justice grant to develop computer-assisted
handwriting-analysis tools for forensic applications, the finding could
be significant for other court cases in which handwritten documents provide
relevant evidence.
Efforts
to analyze handwriting in criminal or civil cases have involved obtaining
samples of writing from potential suspects or witnesses and then comparing
them with the handwriting in question. But several Supreme Court decisions,
such as Daubert v. Merrell Dow, require that all expert testimony, including
testimony about document examination, must meet scientifically rigorous
criteria. Because few, if any, objective criteria have existed for handwriting
analysis, testimony concerning handwritten documents often has not been
admitted in testimony.
The
UB research is the first to provide such objective criteria.
"We
set out to answer on a scientific basis the question, 'Is the handwriting
of different individuals truly distinct?' The answer is 'Yes,'" said Sargur
Srihari, SUNY Distinguished Professor in the Department of Computer Science
and Engineering, and director of the Center of Excellence in Document
Analysis and Recognition (CEDAR).
CEDAR
is the largest research center in the world devoted to developing new
technologies that can recognize and read handwriting. In the U.S., it
is the only center at a university where researchers in artificial intelligence
apply pattern-recognition techniques to the problem of reading handwriting.
Over
the past decade, CEDAR has worked with the U.S. Postal Service developing
and refining the software now in use in postal distribution centers across
the nation that allows up to 70 percent of the handwritten addresses on
envelopes to be read by sorting machines.
That
expertise in teaching machines to read handwritten letters and numbers
attracted the attention of the NIJ, which was interested in a different
problem: finding out not what a written document said, but rather the
identity of the writer.
The
UB team developed a software system, based on an analysis that identified
features from each of 1,500 handwriting samples and assigned a value to
each feature.
Based
on those values, the system is able to distinguish with 96 percent confidence
whether two documents were written by the same person or different people.
Srihari
added that the team's ability to answer the question with such a high
confidence rate implies that there is a significant amount of variation
between the handwriting of individuals.
The
UB researchers solicited cursive handwriting samples of the same three
documents from 1,500 individuals representative of the distribution of
different genders, age groups and ethnicities in the general population.
The
source documents were designed to capture a wide range of attributes of
handwritten English, such as variations in the positions of letters, numbers
and punctuation marks, and certain combinations of letters and numbers.
The
researchers extracted features relevant to the entire document, to specific
paragraphs in the document, to single words of the document and even single
characters.
Instead
of analyzing the documents visually, the way a human expert would, Srihari
said their software system deconstructed each sample. The system extracted
11 features that characterize the overall structure of the writing, such
as the layout of the document and spacing of each line, and 512 features
of individual characters, such as stroke marks.
Co-authors
include Sung-Hyuk Cha, formerly a UB doctoral candidate, now an assistant
professor at Pace University; Hina Arora, research scientist at IBM, and
Sangjik Lee, a doctoral candidate in the UB Department of Computer Science
and Engineering.
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