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Preliminary Design and Application Prospect of Single Chinese Character Calligraphy Image Scoring Algorithm

  • Shutang Liu
  • Zhen Wang
  • Chuansheng Wang
  • Junxian Zheng
  • Fuquan ZhangEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)

Abstract

This paper improves the image classification task based on deep learning and proposes a new font grading system to help calligraphy lovers to practice calligraphy. The basic model of the framework proposed in this paper is ResNet, and then dilated convolution, deformable convolutional, and deformable pooling are used on the traditional ResNet to improve performance. Experimental results show that the proposed algorithm can make a reasonable judgment on handwriting.

Keywords

Chinese character calligraphy Calligraphy image scoring 

Notes

Acknowledgements

The paper is supported by the foundation of Fujian Province Educational Science “Thirteenth Five-Year Plan” 2018 Project—“Research on the college students’ anomie of online courses learning and intervention of their online courses learning” (No. FJJKCGZ18-850, Key funding project), Young and Middle-aged Teacher Educational and Scientific Research Project of Fujian Province—“Research on the college students’ anomie of online courses learning and intervention of their online courses learning”, and the Teaching Reform Research Project of Minjiang University in 2018—“The Interventional Teaching Reform aimed at the online courses learning anomie of college students” (No. MJU2018A005).

References

  1. 1.
    Mi, W.: The e-curriculum development: a new way for current primary and secondary school calligraphy teaching. Curric., Teach. Mater. Method 38(07), 87–91 (2018)Google Scholar
  2. 2.
    Ministry of Education of the People’s Republic of China official website, http://www.moe.gov.cn/srcsite/A08/s5664/s7209/s6872/201807/t20180725_343681.html. Last accessed 24 July 2018
  3. 3.
    Zhou, Y.: Thoughts on the construction of online open courses for art. Art Educ. 336(20), 136–137 (2018)Google Scholar
  4. 4.
    He, K.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 770–778 (2016)Google Scholar
  5. 5.
    Yu, F.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122(2015)
  6. 6.
    Dai, J.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)Google Scholar
  7. 7.
    Fanello, S.R.: Keep it simple and sparse: real-time action recognition. J. Mach. Learn. Res. 14(1), 2617–2640 (2017)Google Scholar
  8. 8.
    Lu, C.: Two-class weather classification. IEEE Trans. Pattern Anal. Mach. Intell. (99), 1 (2017)Google Scholar
  9. 9.
    Woitek, R.: A simple classification system (the Tree flow chart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions. Eur. Radiol. 27(9), 3799–3809 (2017)CrossRefGoogle Scholar
  10. 10.
    Cicero, M.: Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Investig. Radiol. 52(5), 281 (2017)CrossRefGoogle Scholar
  11. 11.
    Yuan, Y.: Hyper spectral image classification via multitask joint sparse representation and stepwise MRF optimization. IEEE Trans. Cybern. 46(12), 2966–2977 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shutang Liu
    • 1
  • Zhen Wang
    • 1
  • Chuansheng Wang
    • 2
  • Junxian Zheng
    • 1
  • Fuquan Zhang
    • 1
    Email author
  1. 1.Minjiang University, Fuzhou University TownFuzhouPeople’s Republic of China
  2. 2.Harbin University of Science and TechnologyHarbinPeople’s Republic of China

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