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Font Generation Method based on U-net

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Published:17 March 2021Publication History

ABSTRACT

The main task of the font design is to design a suitable font according to the actual application scenario, which has extremely wide commercial application value. Generally the traditional font design requires professionals to design, with longer design time, lower work efficiency, and higher labor costs. Font design is essentially a problem of image synthesis. U-net is a deep learning network structure, which has been widely used in image synthesis, but the images synthesized by U-net have the disadvantages of low image quality and poor visual effects. In order to improve the shortcomings of U-net image synthesis effectively, this paper provides an improved U-net method for better font design. The new method is called Swish-gated residual dilated U-net (Swish-gated residual dilated U-net, SGRDU). In SGRDU, the proposed swish layer and swish-gated residual block can effectively control the information transmitted by each horizontal and vertical layer in U-net, and accelerate the network convergence. Dilated convolution is used to improve the perception of the network. Experimental results show that, compared with other residual U-net, the font synthesized by SGRDU has better visual effect and quality.

References

  1. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Liang Peijun, Liu Yijun. Coloring method of hand-drawn cartoons based on conditional generation of confrontation networks [J]. Computer Application Research, 2019.36(01): 308-311.Google ScholarGoogle Scholar
  3. Wu Xiaoqi. Research on manga style transfer method based on deep learning [D]. Xi'an University of Technology, 2019.Google ScholarGoogle Scholar
  4. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).Google ScholarGoogle Scholar
  5. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Vald´esHern´andez, M., Gonz´alez-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506– 517. Springer, Cham (2017).Google ScholarGoogle Scholar
  6. Liu G., Chen X., Hu Y. (2019) Anime Sketch Coloring with Swish-Gated Residual U-Net. In: Peng H., Deng C., Wu Z., Liu Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, SingaporeGoogle ScholarGoogle Scholar
  7. Fisher Y.Vladlen K.:Multi-Scale Context Aggregation by Dilated Convolutions .arXiv:1511.07122[cs.CV](2016)Google ScholarGoogle Scholar
  8. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016)Google ScholarGoogle Scholar
  9. .Lin, B.S., Michael, K., Kalra, S., Tizhoosh, H.R.: Skin lesion segmentation: U-nets versus clustering. In: Proceedings of 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017), Honolulu, HI, United States, pp. 1–7, November 2017Google ScholarGoogle ScholarCross RefCross Ref
  10. Zhao, H., Sun, N.: Improved U-net model for nerve segmentation. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 496–504. Springer, Cham (2017).Google ScholarGoogle Scholar
  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, United states, pp. 770–778 (2016)Google ScholarGoogle ScholarCross RefCross Ref
  12. Ramachandran, P., Zoph, B., Le, Q.V.: Searching for activation functions. CoRR abs/1710.05941 (2017).Google ScholarGoogle Scholar
  13. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectififiers: surpassing humanlevel performance on imagenet classifification. In: Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV 2015), Santiago, Chile, pp. 1026– 1034 (2015)Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. CoRR abs/1607.06450 (2016).Google ScholarGoogle Scholar
  15. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014).Google ScholarGoogle Scholar
  16. Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual U-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)Google ScholarGoogle ScholarCross RefCross Ref
  17. Huang Jiaheng, Li Xiaowei, Chen Benhui, Yang Dengqi. A comparative study of image similarity algorithms based on hash [J]. Journal of Dali University, 2017, 2(12): 32-37.Google ScholarGoogle Scholar
  18. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

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    CSAI '20: Proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence
    December 2020
    294 pages
    ISBN:9781450388436
    DOI:10.1145/3445815

    Copyright © 2020 ACM

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    Publication History

    • Published: 17 March 2021

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