A novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition

Abstract

To solve the problems of large number of similar Chinese characters, difficult feature extraction and inaccurate recognition, we propose a novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition. First, we design a deep convolutional neural network model for feature grouping extraction and fusion. The multilevel stacked feature group extraction module is used to extract the deep abstract feature information of the image and carry out the fusion between the different feature information modules. Secondly, we use the designed down-sampling and channel amplification modules to reduce the feature dimension while preserving the important information of the image. The feature information is refined and condensed to solve the overlapping and redundant problem of feature information. Thirdly, inter-layer feature fusion algorithm and Softmax classification function constrained by L2 norm are used. We further compress the parameter clipping to avoid the loss of too much accuracy due to the clipping of important parameters. The dynamic network surgery algorithm is used to ensure that the important parameters of the error deletion are reassembled. Experimental results on public data show that the designed recognition model in this paper can effectively improve the recognition rate of handwritten Chinese characters.