Statistical characterization of handwriting characteristics using automated tools

Gregory R. Ball; Sargur N. Srihari

doi:10.1117/12.876536

24 January 2011 Statistical characterization of handwriting characteristics using automated tools

Gregory R. Ball, Sargur N. Srihari

Proceedings Volume 7874, Document Recognition and Retrieval XVIII; 78740H (2011) https://doi.org/10.1117/12.876536
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

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.

Citation Download Citation

Gregory R. Ball and Sargur N. Srihari "Statistical characterization of handwriting characteristics using automated tools", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740H (24 January 2011); https://doi.org/10.1117/12.876536

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