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Volume 41 Issue 4
Apr.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(4): 751-756.
PAN Weishen, JIN Lianwen, FENG Ziyonget al. Recognition of Chinese characters based on multi-scale gradient and deep neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(4): 751-756. doi: 10.13700/j.bh.1001-5965.2014.0499(in Chinese)
Citation: PAN Weishen, JIN Lianwen, FENG Ziyonget al. Recognition of Chinese characters based on multi-scale gradient and deep neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(4): 751-756. doi: 10.13700/j.bh.1001-5965.2014.0499(in Chinese)
shu

Recognition of Chinese characters based on multi-scale gradient and deep neural network

doi: 10.13700/j.bh.1001-5965.2014.0499

  • Department of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China

  • Received Date: 28 Apr 2014
  • Rev Recd Date: 27 Nov 2014
  • Publish Date: 20 Apr 2015
    Abstract
  • The method to extract the gradient histogram feature of the Chinese characters with a multi-scale sliding window and to recognize the printed Chinese characters with deep neural network was presented. In order to acquire the spatial information of the gradient histogram, a retractable sliding window technique was proposed for segmenting the images and getting the gradient feature information from different scales which can effectively combine all the global features and local block features of Chinese characters. The experiment was carried out by using a 5-layer deep neural network to classify 3755 categories of printed Chinese characters.A Dropout technique was applied so as to prevent over-fitting training and to improve the generalization ability of the neural network. The accuracy of the experiment reaches 98.292%, which has better recognition performance and demonstrates that the method of applying a multi-scale gradient feature and deep neural network model on the recognition of Chinese characters is effective.

     

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