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Off-Line Handwritten Character Recognition System Using Support Vector Machine

Received: 24 October 2017     Accepted: 21 November 2017     Published: 13 December 2017
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Abstract

Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 2)
DOI 10.11648/j.ajnna.20170302.12
Page(s) 22-28
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Handwritten Character Recognition, Support Vector Machine, Multi Layer Perceptron, And Feature Extraction

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Cite This Article
  • APA Style

    Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz. (2017). Off-Line Handwritten Character Recognition System Using Support Vector Machine. American Journal of Neural Networks and Applications, 3(2), 22-28. https://doi.org/10.11648/j.ajnna.20170302.12

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    ACS Style

    Gauri Katiyar; Ankita Katiyar; Shabana Mehfuz. Off-Line Handwritten Character Recognition System Using Support Vector Machine. Am. J. Neural Netw. Appl. 2017, 3(2), 22-28. doi: 10.11648/j.ajnna.20170302.12

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    AMA Style

    Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz. Off-Line Handwritten Character Recognition System Using Support Vector Machine. Am J Neural Netw Appl. 2017;3(2):22-28. doi: 10.11648/j.ajnna.20170302.12

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  • @article{10.11648/j.ajnna.20170302.12,
      author = {Gauri Katiyar and Ankita Katiyar and Shabana Mehfuz},
      title = {Off-Line Handwritten Character Recognition System Using Support Vector Machine},
      journal = {American Journal of Neural Networks and Applications},
      volume = {3},
      number = {2},
      pages = {22-28},
      doi = {10.11648/j.ajnna.20170302.12},
      url = {https://doi.org/10.11648/j.ajnna.20170302.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170302.12},
      abstract = {Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Off-Line Handwritten Character Recognition System Using Support Vector Machine
    AU  - Gauri Katiyar
    AU  - Ankita Katiyar
    AU  - Shabana Mehfuz
    Y1  - 2017/12/13
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    N1  - https://doi.org/10.11648/j.ajnna.20170302.12
    DO  - 10.11648/j.ajnna.20170302.12
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
    SP  - 22
    EP  - 28
    PB  - Science Publishing Group
    SN  - 2469-7419
    UR  - https://doi.org/10.11648/j.ajnna.20170302.12
    AB  - Selection of classifiers and feature extraction methods has a prime role in achieving best possible classification accuracy in character recognition system. Issues of character recognition system related to choice of classifiers and feature extraction methods can be resolved through these objectives. In this proposed work an efficient Support Vector Machine based off-line handwritten character recognition system has been developed. The experiments have been performed using well known standard database acquired from CEDAR, also seven different approaches of feature extraction techniques have been proposed to construct the final feature vector. It is evident from the experimental results that the performance of Support Vector Machine outperforms other state of art techniques reported in literature.
    VL  - 3
    IS  - 2
    ER  - 

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Author Information
  • Department of Electrical and Electronics Engineering, ITS Engineering College, Gr. Noida (U.P.), India

  • School of Computer Science and Engineering, VIT University, Vellore (T.N.), India

  • Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India

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