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  • ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015)

    Author(s)
    Malik, MI
    Ahmed, S
    Marcelli, A
    Pal, U
    Blumenstein, M
    Alewijns, L
    Liwicki, M
    Griffith University Author(s)
    Blumenstein, Michael M.
    Year published
    2015
    Metadata
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    Abstract
    This paper presents the results of the ICDAR 2015 competition on signature verification and writer identification for on- and off-line 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 and training and evaluation data are collected and provided by FHEs and PR-researchers. Four tasks are defined for four different languages; Bengali off-line signature verification, Italian off-line signature verification, German on-line ...
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    This paper presents the results of the ICDAR 2015 competition on signature verification and writer identification for on- and off-line 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 and training and evaluation data are collected and provided by FHEs and PR-researchers. Four tasks are defined for four different languages; Bengali off-line signature verification, Italian off-line signature verification, German on-line signature verification, and English handwritten text based writer identification. In total, 40 systems have participated in this competition. The participants of the signatures modality were motivated to report their results in Likelihood Ratios (LRs). This has made the systems even more interesting for application in forensic casework. For evaluating the performance of the systems, we have used the forensically substantial Cost of Log Likelihood Ratios (Ĉllr) in the case of signatures, and the F-measure in the case of handwritten text.
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    Conference Title
    Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
    Volume
    2015-November
    DOI
    https://doi.org/10.1109/ICDAR.2015.7333948
    Subject
    Artificial intelligence not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/340500
    Collection
    • Conference outputs

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