Study On Chinese Character Recognition System Based On Multi-Algorithm Integration & Neural Network

Handwritten (off-line) Chinese character recognition (HCCR) has become favorable area in OCR by now,and is the most difficult project in computer character recognition too. After analyzing the currently up-to-date techniques for Chinese character recognition,in this paper,we propose a Chinese character recognition system based on multi-algorithm integration and neural network. According to the successful application to pattern recognition of small category for neural network,in this system,we use a distance classifier based on gross periphery feature for rough classification in order to classify the total Chinese character set to some small sets,and then a BP network classifier based on the probability distribution of pixels with elastic meshing is used for fine recognition.In this paper,first,the five modules in the system are explained in detail including the input of Chinese character,preprocessing,rough classification,fine classification and post-processing. Especially as to the neural network classifier,we not only discuss the fundamental principle of BP network,feature extraction,the realization of BP network,the selection of network structure and parameters,but also discuss its drawbacks and its improved solutions.Secondly,based on the neural network toolbox,a convenient realization on MATLAB is discussed for BP neural network,and the programming methods are presented about how to create a network,train a network and simulate a network.Lastly,by use of MAILAB,preparatory experiment on total 100 samples of 10 categories of handwritten Chinese characters produced the result of recognition rate of 95%,showing that using neural network for HCCR is feasible and promising. It will be of great importance to direct the establishment of practical HCCR system.