Recognition of handwritten Chinese characters by self-growing probabilistic decision-based neural networks
Hsin-Chia Fu, Yeong-Yuh Xu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
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.