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  • The 4NSigComp2010 off-line signature verification competition: Scenario 2

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    Author(s)
    Blumenstein, Michael
    A. Ferrer, Miguel
    argas, J.
    Griffith University Author(s)
    Blumenstein, Michael M.
    Year published
    2010
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    Abstract
    The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises genuine, simulated signatures produced by unskilled imitators or random signatures (genuine signatures from other writers). Ten systems were evaluated, and some interesting results are presented in terms of accuracy and execution ...
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    The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises genuine, simulated signatures produced by unskilled imitators or random signatures (genuine signatures from other writers). Ten systems were evaluated, and some interesting results are presented in terms of accuracy and execution time. The top ranking system attained an overall error of 8.94%. This result interestingly correlates with the top ranking accuracy achieved in a previous signature verification competition at ICDAR 2009.
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    Conference Title
    12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2010)
    DOI
    https://doi.org/10.1109/ICFHR.2010.117
    Copyright Statement
    © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/37832
    Collection
    • Conference outputs

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