Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Discriminatory Power of Handwritten Words for Writer Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A Statistical Model For Writer Verification
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A writer identification and verification system using HMM based recognizers
Pattern Analysis & Applications
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Forensic document verification presents a different and interesting set of challenges as opposed to traditional writer identification and verification tasks using natural handwriting. The handwritten data presented to a forensic examiner is often deliberately altered, in addition to being limited in quantity. Specifically, the alterations can be either forged, where one imitates another person's handwriting; or repudiated, where one deliberately distorts his handwriting in order to avoid identification. In this paper, we present a framework to detect repudiation in forensic documents, where we only have one pair of documents to arrive at a decision. The approach generates a statistically significant confidence score from matching two documents, which can be used to screen the documents that are passed on to an expert examiner. The approach can be extended for detection of forgeries as well.