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Proceedings Paper

A handwritten numeral recognition algorithm based on independent components analysis
Author(s): Lai Jiang; Zhen Ji; Li Zhang; Jihong Zhang
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Paper Abstract

Independent Components Analysis (ICA) is an effective approach of blind source separation and has been received much more attention because of its potential application in signal processing such as telecommunication and image processing. Feature extraction of images has been also focused as one of prominent applications of ICA. Nine Stroke Density (NSD) feature extraction method will provide sufficient information to the recognition engine. Several other feature extraction methods are discussed and compared to stroke density method in detail. ICA extracts the underlying statistically independent components from a mixture of the NSD feature vectors. These independent components are feed into the neural netowrk for the recognition purpose. The experiment results show that ICA performs well for feature extraction and this proposed method is more effective in recognizing handwriting character than merely using neural networks directly.

Paper Details

Date Published: 25 September 2003
PDF: 4 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538845
Show Author Affiliations
Lai Jiang, Shenzhen Univ. (China)
Zhen Ji, Shenzhen Univ. (China)
Li Zhang, Shenzhen Univ. (China)
Jihong Zhang, Shenzhen Univ. (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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