Chinese character recognition based on Gabor feature extraction and CNN

Yudian Xiong; Tongwei Lu; Yongyuan Jiang

doi:10.1117/12.2288374

8 March 2018 Chinese character recognition based on Gabor feature extraction and CNN

Yudian Xiong, Tongwei Lu, Yongyuan Jiang

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091N (2018) https://doi.org/10.1117/12.2288374
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Citation Download Citation

Yudian Xiong, Tongwei Lu, and Yongyuan Jiang "Chinese character recognition based on Gabor feature extraction and CNN", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091N (8 March 2018); https://doi.org/10.1117/12.2288374

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