Research of the properties of receptive field in handwritten Chinese character recognition based on DCNN model

For problem of influence of different size of receptive fields to DCNN modeling on offline handwritten Chinese character recognition (HCCR), the relationships of the receptive field, the number of parameter, the number of layer, the number of feature map and the size of the area occupied by the basic strokes of Chinese characters have been deeply researched and verified with experiment. With a Softmax classifier of output layer, GPU techniques are applied to accelerate model training and Drop-out method is adopted to prevent over-fitting. The research results of the theory and the experiment are important reference in the light of reasonable or effective selection of receptive field size for DCNN model in HCCR applications. It also provides a method for selecting the size of the receptive field for DCNN in HCCR.