Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

Handwritten Digit Recognition Based on Support Vector Machine

Authors
Xinwen Gao, Benbo Guan, Liqing Yu
Corresponding Author
Xinwen Gao
Available Online July 2015.
DOI
10.2991/icismme-15.2015.198How to use a DOI?
Keywords
Handwriting recognition; Support Vector Machine; Feature extraction; Normalization; Kernel function.
Abstract

In this paper, we propose a handwritten digit recognition method based on Support Vector Machined (SVM). Firstly, some main features are extracted from the handwritten digital images (Euler Number, roundness, moment feature, crossing density, pixel density). Secondly, adopt the method of SVM for classification. This paper adds the feature values normalized, using radial basis function and Cross-validation important parameters. Our approach has been implemented with MNIST database and we have achieved an average recognition rate of 96.3%, the lowest single digit recognition rate of 93.5% when the training data are 100×10.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-62520-67-7
ISSN
1951-6851
DOI
10.2991/icismme-15.2015.198How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xinwen Gao
AU  - Benbo Guan
AU  - Liqing Yu
PY  - 2015/07
DA  - 2015/07
TI  - Handwritten Digit Recognition Based on Support Vector Machine
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 940
EP  - 943
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.198
DO  - 10.2991/icismme-15.2015.198
ID  - Gao2015/07
ER  -