Generating Realistic Chinese Handwriting Characters via Deep Convolutional Generative Adversarial Networks

  • Chenkai GuEmail author
  • Jin Liu
  • Lei Kong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)


A person can hardly write a totally same handwriting character, more or less, there will be some tiny difference between each character. Usually, we use a neural network to generate handwriting characters, but each time we want this model to output a character, it will always the totally same. To solve this tiny different problem, we use a special neural network called DCGANs (deep convolutional generative adversarial networks). Experiments show that our method achieves good performance.


Realistic characters Generate model DCGANs 


  1. 1.
    Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Haines, T.S.F., Mac Aodha, O., Brostow, G.J.: My text in your handwriting. ACM Trans. Graph. 35(3), 26 (2016)CrossRefGoogle Scholar
  3. 3.
    Xu, S., Jin, T., Jiang, H., et al.: Automatic generation of personal Chinese handwriting by capturing the characteristics of personal handwriting. In: Conference on Innovative Applications of Artificial Intelligence, DBLP, Pasadena, California, USA, July 14–16 (2009)Google Scholar
  4. 4.
    Zhang, X.Y., Yin, F., Zhang, Y.M., et al.: Drawing and Recognizing Chinese Characters with Recurrent Neural Network (2016)Google Scholar
  5. 5.
    Lian, Z., Zhao, B., Xiao, J.: Automatic generation of large-scale handwriting fonts via style learning. In: SIGGRAPH ASIA 2016, Technical Briefs. ACM, 12 (2016)Google Scholar
  6. 6.
    Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)CrossRefGoogle Scholar
  7. 7.
    Goodfellow, I.J., Pougetabadie, J., Mirza, M., et al.: Generative adversarial nets. Adv. Neural. Inf. Process. Syst. 3, 2672–2680 (2014)Google Scholar
  8. 8.
    Lee, Y., Jung, K., Lee, D.: Generative Adversarial Networks for Generating Hand Drawn ShapesGoogle Scholar
  9. 9.
    Radford. A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Computer Science (2015)Google Scholar
  10. 10.
    Krizhevsky A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems. Curran Associates Inc., pp. 1097–1105 (2012)Google Scholar
  11. 11.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on Machine Learning, DBLP, pp. 807–814 (2010)Google Scholar
  12. 12.
    Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784 (2014)

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiChina
  2. 2.The Third Branch of Shanghai Administration InstituteShanghaiChina

Personalised recommendations