Off-Line Handwritten Character Recognition System Using Support Vector Machine

Gauri Katiyar; Ankita Katiyar; Shabana Mehfuz

doi:doi:10.11648/j.ajnna.20170302.12

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Off-Line Handwritten Character Recognition System Using Support Vector Machine

Gauri Katiyar, Ankita Katiyar, Shabana Mehfuz

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 2)

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), 2024. Published by Science Publishing Group

Keywords

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

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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
PY  - 2017
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

Gauri Katiyar

Department of Electrical and Electronics Engineering, ITS Engineering College, Gr. Noida (U.P.), India
Ankita Katiyar

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

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

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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

Copy | Download

@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}
}

Copy | Download

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
PY  - 2017
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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