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Proceedings Paper

Robust recognition of printed Chinese characters using multilayer perceptron and Walsh functions
Author(s): Kou-Yuan Huang; Hsiang-Tsun Yen
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Paper Abstract

In this paper, a neural network approach for the recognition of printed Chinese characters is developed. A multi-layer perceptron is trained as the classifier by using the back-propagation algorithm, and the Walsh functions are employed for feature extraction. Thirty similar Chinese characters (classes) are designed in the experimental domain. The network is initially trained with noisefree training samples, and is retrained gradually with misclassified noisy testing patterns to improve the robustness of the classifier. Through classifying a large set of 9000 unknown testing patterns of various noise degrees, a great augmentation in system robustness and an encouraging recognition performance are presented.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139986
Show Author Affiliations
Kou-Yuan Huang, National Chiao Tung Univ. (Taiwan)
Hsiang-Tsun Yen, National Chiao Tung Univ. (Taiwan)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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