Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74549-5_38 | ICB |
Keywords | Field | DocType |
verification task,forensic examiner,forensic document,forensic document verification,natural handwriting,repudiation detection,interesting set,handwritten data,handwritten document,traditional writer identification,expert examiner,significant confidence score,statistical significance | Confidence score,Reference Document,Pattern recognition,Handwriting,Computer science,Speech recognition,Artificial intelligence,Natural language processing,False accept rate | Conference |
Volume | ISSN | ISBN |
4642 | 0302-9743 | 3-540-74548-3 |
Citations | PageRank | References |
2 | 0.37 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sachin Gupta | 1 | 2 | 0.37 |
| Anoop M. Namboodiri | 2 | 255 | 26.36 |