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Proceedings Paper

Chinese character recognition based on Gabor feature extraction and CNN
Author(s): Yudian Xiong; Tongwei Lu; Yongyuan Jiang
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Paper 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.

Paper Details

Date Published: 8 March 2018
PDF: 5 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091N (8 March 2018);
Show Author Affiliations
Yudian Xiong, Wuhan Institute of Technology (China)
Tongwei Lu, Wuhan Institute of Technology (China)
Yongyuan Jiang, Wuhan Institute of Technology (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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