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dc.contributor.convenorDavid Doermann
dc.contributor.authorMalik, MI
dc.contributor.authorLiwicki, M
dc.contributor.authorAlewijnse, L
dc.contributor.authorOhyama, W
dc.contributor.authorBlumenstein, M
dc.contributor.authorFound, B
dc.contributor.editorElisa H Barney Smith, Abdel Belaid, Koichi Kise
dc.date.accessioned2017-12-19T01:00:33Z
dc.date.available2017-12-19T01:00:33Z
dc.date.issued2013
dc.date.modified2014-05-30T03:19:28Z
dc.identifier.issn1520-5363
dc.identifier.refuriwww.icdar2013.org
dc.identifier.doi10.1109/ICDAR.2013.220
dc.identifier.urihttp://hdl.handle.net/10072/59652
dc.description.abstractThis 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 (C^llr). The system having the smallest value of the Minimum Cost of Log Likelihood Ratio (C^llrmin) 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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameICDAR 2013
dc.relation.ispartofconferencetitleProceedings of the International Conference on Document Analysis and Recognition, ICDAR
dc.relation.ispartofdatefrom2013-08-25
dc.relation.ispartofdateto2013-08-28
dc.relation.ispartoflocationWashington DC, United States
dc.relation.ispartofpagefrom1477
dc.relation.ispartofpageto1483
dc.rights.retentionY
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleICDAR2013 competitions on signature verification and writer identification for on- and offline skilled forgeries (SigWiComp2013)
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.


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