Recognition of handwritten numeric characters using neural networks designed on approximate reasoning architecture

We have newly developed a handwritten numeric character recognition system with neural networks based on an approximate reasoning architecture (NARA). Handwritten character recognition is one of the most difficult tasks in an area of pattern recognition because of the variation of handwritten images even in a same category of character. NARA, which consists of a classifier of input data, several sub-neural networks and an integrator of the outputs of sub-neural networks can realize a stable recognition of large variations of handwritten character images, and achieved a correct answer rate of 95.41%, an error rate of 0.20% and a rejection rate of 4.38%.