Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network |
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Authors: | Seong-Whan Lee Chang-Hun Kim Hong Ma Yuan Y. Tang |
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Affiliation: | †Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul 136–701, Korea;§Department of Mathematics, Sichuan University, Chengdu, Sichuan 610064, People's Republic of China;∥Department of Computing Studies, Hong Kong Baptist University, Waterloo Road, Kowloon, Hong Kong |
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Abstract: | In this paper, we propose a new scheme for multiresolution recognition of unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: a feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, Electro-Technical Laboratory of Japan, and Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes. |
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Keywords: | Multiresolution recognition Handwritten numeral recognition Wavelet transform Multilayer cluster neural network |
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