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/).
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 -