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Proceedings Paper

Recognition of handwritten Chinese characters by self-growing probabilistic decision-based neural networks
Author(s): Hsin-Chia Fu; Y. Y. Xu
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Paper Abstract

In this paper, we present a Bayesian decision-based neural networks (BDNN) for handwritten Chinese character recognition. The proposed Self-growing Probabilistic Decision-based Neural Networks (SPDNN) adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Our prototype system demonstrates a successful utilization of SPDNN to the handwriting of Chinese character recognition on the public databases, CCL/HCCR1 and in house database (NCTU/NNL). Regarding the performance, experiments on three different databases all demonstrated high recognition (86 - 94%) accuracy as well as low rejection/acceptance (6.7%) rates. As to the processing speed, the whole recognition process (including image preprocessing, feature extraction, and recognition) consumes approximately 0.27 second/character on a Pentium-100 based personal computer, without using hardware accelerator or co-processor.

Paper Details

Date Published: 18 June 1998
PDF: 12 pages
Proc. SPIE 3422, Input/Output and Imaging Technologies, (18 June 1998); doi: 10.1117/12.311079
Show Author Affiliations
Hsin-Chia Fu, National Chiao Tung Univ. (Taiwan)
Y. Y. Xu, National Chiao Tung Univ. (Taiwan)

Published in SPIE Proceedings Vol. 3422:
Input/Output and Imaging Technologies
Yusheng Tim Tsai; Teh-Ming Kung; Jan Larsen, Editor(s)

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