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  • Multi-Script Off-line Signature Identification

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    84174_1.pdf (484.0Kb)
    Author(s)
    Pal, Srikanta
    Alireza, Alaei
    Pal, Umapada
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Pal, Srikanta
    Year published
    2012
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    Abstract
    In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of ...
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    In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of 2100 Bangla signatures, 2100 Hindi signatures and 2100 English signatures are used for experimentation. Two different results based on two different feature sets are calculated and analysed. The highest accuracy of 92.14% is obtained based on the gradient features using 4200 (1400 Bangla +1400 Hindi + 1400 English) samples for training and 2100 (700 Bangla +700 Hindi +700 English) samples for testing.
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    Conference Title
    Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems (HIS)
    Publisher URI
    http://www.mirlabs.net/his12/
    DOI
    https://doi.org/10.1109/HIS.2012.6421340
    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
    Image Processing
    Publication URI
    http://hdl.handle.net/10072/49651
    Collection
    • Conference outputs

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