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Skeleton-based Generative Adversarial Networks for Font Shape Style Transfer: Learning text style from some characters and transferring the style to any unseen characters

Published: 19 June 2023 Publication History

Abstract

This paper presents a new font shape style transfer technique that employs a generative adversarial network (GAN) and skeleton-based input feature maps to modify a target text to match a target font shape while retaining the original text content. Our GAN model is modified from a Shape-Matching GAN which utilizes a StyleNet generator and a PatchGAN discriminator. Rather than using a base-font character images as input to the generator like other existing font transfer models, we utilize the proposed skeleton-based features as input. The experimental results show that our model can produce the unseen characters in the desired font style better than an existing method.

References

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S. Azadi, M. Fisher, V. Kim, Z. Wang, E. Shechtman, and T. Darrell, “Multi-Content GAN for Few-Shot Font Style Transfer,” 2018. [Online]. Available: https://github.com/azadis/
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S. Yang, Z. Wang, Z. Wang, N. Xu, J. Liu, and Z. Guo, “Controllable Artistic Text Style Transfer via Shape-Matching GAN,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 4442-4451
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          CVIPPR '23: Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition
          April 2023
          93 pages
          ISBN:9798400700033
          DOI:10.1145/3596286
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          Association for Computing Machinery

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          Publication History

          Published: 19 June 2023

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          Author Tags

          1. Font Style
          2. Generative Adversarial Network
          3. Style Transfer
          4. Text Skeleton

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          • Research-article
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          • Refereed limited

          Funding Sources

          • Faculty of Engineering, Chiang Mai University
          • Graduate School, Chiang Mai University

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          CVIPPR 2023

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          CVIPPR '23 Paper Acceptance Rate 14 of 38 submissions, 37%;
          Overall Acceptance Rate 14 of 38 submissions, 37%

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