Invited Paper Recognition of Handwritten Chinese Characters by Self-growing Probabilistic Decision-based Neural Networks*

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 pro-totype 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 (86e-'94%) accuracy as well as low rejection/acceptance ( 6.7%) rates. As to the processing speed, the whole recognition process (including image preprocessing, feature ex-traction, and recognition) consumes approximately 0.27 second/character on a Pentium-100 based personal computer, without using hardware accelerator or co-processor