Chinese character recognition by the auto recognition system

It is well known that neural networks (NN) have a capability to extract characteristics from input data. So the research into application of NNs for pattern recognition has been very active. However, lots of researches look at recognition for character patterns for under one hundred cases, few researches look at recognition of thousands of character patterns. However, in the case of dealing with general recognition problems, the number of learning data increase. Thus is it necessary to adapt NNs for this problem. In this paper, the authors propose the auto recognition system which can recognize 6709 Chinese characters by plural combined NNs. The system has four parts, rough classification part, fine classification part, recognition part, and auto judgement part. The system classifies input data by character density at the rough and fine classification parts, and recognizes by the multi-layered NN(BPNET) at the recognition part. The auto judgement part judges and sends out Chinese character from four different BPNET´s output values. As a result, the authors get 100% recognition rate for 6709 Chinese characters