
Proceedings Paper
Statistical characterization of handwriting characteristics using automated toolsFormat | Member Price | Non-Member Price |
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Paper Abstract
We provide a statistical basis for reporting the results of handwriting examination by questioned document (QD)
examiners. As a facet of Questioned Document (QD) examination, the analysis and reporting of handwriting
examination suffers from the lack of statistical data concerning the frequency of occurrence of combinations of
particular handwriting characteristics. QD examiners tend to assign probative values to specific handwriting
characteristics and their combinations based entirely on the examiner's experience and power of recall. The
research uses data bases of handwriting samples that are representative of the US population. Feature lists of
characteristics provided by QD examiners, are used to determine as to what frequencies need to be evaluated.
Algorithms are used to automatically extract those characteristics, e.g., a software tool for extracting most
of the characteristics from the most common letter pair th, is functional. For each letter combination the
marginal and conditional frequencies of their characteristics are evaluated. Based on statistical dependencies
of the characteristics the probability of any given letter formation is computed. The resulting algorithms are
incorporated into a system for writer verification known as CEDAR-FOX.
Paper Details
Date Published: 24 January 2011
PDF: 7 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740H (24 January 2011); doi: 10.1117/12.876536
Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)
PDF: 7 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740H (24 January 2011); doi: 10.1117/12.876536
Show Author Affiliations
Gregory R. Ball, State Univ. of New York at Buffalo (United States)
Sargur N. Srihari, State Univ. of New York at Buffalo (United States)
Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)
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