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Proceedings Paper

Handwritten digits recognition based on immune network
Author(s): Yangyang Li; Yunhui Wu; Lc Jiao; Jianshe Wu
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Paper Abstract

With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

Paper Details

Date Published: 2 December 2011
PDF: 8 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80041A (2 December 2011); doi: 10.1117/12.902892
Show Author Affiliations
Yangyang Li, Xidian Univ. (China)
Yunhui Wu, Xidian Univ. (China)
Lc Jiao, Xidian Univ. (China)
Jianshe Wu, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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