ICDAR 2013 competitions on signature verification and writer identification for on- and offline skilled forgeries (SigWiComp 2013)

Muhammad Imran Malik, Marcus Liwicki, Linda Alewijnse, Wataru Ohyama, Michael Blumenstein, Bryan Found

Research output: Contribution to journalConference articlepeer-review

65 Citations (Scopus)

Abstract

This paper presents the results of the ICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries jointly organized by PR researchers and Forensic Handwriting Examiners (FHEs). The aim is to bridge the gap between recent technological developments and forensic casework. Two modalities (signatures, and handwritten text) are considered where training and evaluation data (in Dutch and Japanese) were collected and provided by FHEs and PR-researchers. Four tasks were defined where the systems had to perform Dutch offline signature verification, Japanese offline signature verification, Japanese online signature verification, and Dutch writer identification. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LR). This has made the systems even more interesting for application in forensic casework. For evaluation of signatures modality, we used both the traditional Equal Error Rate (EER) and forensically substantial Cost of Log Likelihood Ratios (Cllr). The system having the smallest value of the Minimum Cost of Log Likelihood Ratio (Cllrmin) is declared winner. For evaluation of the handwritten text modality, we used the precision and accuracy measures and winners are announced on the basis of best F-measure value.

Original languageEnglish
Article number6628858
Pages (from-to)1477-1483
Number of pages7
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
DOIs
Publication statusPublished - Dec 11 2013
Event12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States
Duration: Aug 25 2013Aug 28 2013

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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