Handwritten Digit Recognition Based on Support Vector Machine

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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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  -