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  • Off-line Bangla Signature Verification

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    83977_1.pdf (264.8Kb)
    Author(s)
    Pal, Srikanta
    Nguyen, Vu
    Blumenstein, Michael
    Pal, Umapada
    Griffith University Author(s)
    Blumenstein, Michael M.
    Nguyen, Vu
    Pal, Srikanta
    Year published
    2012
    Metadata
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    Abstract
    In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as ...
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    In the field of information security, biometric systems play an important role. Within biometrics, automatic signature identification and verification has been a strong research area because of the social and legal acceptance and extensive use of the written signature as an individual authentication. Signature verification is a process in which the questioned signature is examined in detail in order to determine whether it belongs to the claimed person or not. Despite substantial research in the field of signature verification involving Western signatures, very few works have been dedicated to non-Western signatures such as Chinese, Japanese, Arabic, or Persian etc. In this paper, the performance of an off-line signature verification system involving Bangla signatures, whose style is distinct from Western scripts, was investigated. The Gaussian Grid feature extraction technique was employed for feature extraction and Support Vector Machines (SVMs) were considered for classification. The Bangla signature database employed in the experiments consisted of 3000 forgeries and 2400 genuine signatures. An encouraging accuracy of 90.4% was obtained from the experiments.
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    Conference Title
    Proceedings of the 10th IAPR International Workshop on Document Analysis Systems DAS 2012
    Publisher URI
    http://www.ict.griffith.edu.au/das2012/
    DOI
    https://doi.org/10.1109/DAS.2012.60
    Copyright Statement
    © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/49673
    Collection
    • Conference outputs

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