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18 June 1998 Recognition of handwritten Chinese characters by self-growing probabilistic decision-based neural networks
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Proceedings Volume 3422, Input/Output and Imaging Technologies; (1998) https://doi.org/10.1117/12.311079
Event: Asia Pacific Symposium on Optoelectronics '98, 1998, Taipei, Taiwan
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hsin-Chia Fu and Y. Y. Xu "Recognition of handwritten Chinese characters by self-growing probabilistic decision-based neural networks", Proc. SPIE 3422, Input/Output and Imaging Technologies, (18 June 1998); https://doi.org/10.1117/12.311079
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