25 September 2003 A handwritten numeral recognition algorithm based on independent components analysis
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Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003); doi: 10.1117/12.538845
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China
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.
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Lai Jiang, Zhen Ji, Li Zhang, Jihong Zhang, "A handwritten numeral recognition algorithm based on independent components analysis", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538845; https://doi.org/10.1117/12.538845
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KEYWORDS
Independent component analysis

Feature extraction

Neural networks

Detection and tracking algorithms

Image processing

Optical character recognition

Neurons

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