Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural networks
Hsin-Chia Fu, Yeong-Yuh Xu · 2002
We introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The self-growing probabilistic decision-based neural network (SPDNN) is a probabilistic type neural networks, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we constructed a three stage recognition system. The prototype system demonstrates a successful utilisation of SPDNN to similar handwritten Chinese recognition on the public database CCL/HCCRI (5401 characters /spl times/200 samples). Regarding the performance, the experiments on the CCL/HCCRI database demonstrated a 90.12% of recognition accuracy with no rejection and 94.11% of accuracy with 6.7% rejection rates, respectively.