• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Few-Shot Content-Level Font Generation

Vol. 16, No. 4, April 30, 2022
10.3837/tiis.2022.04.005, Download Paper (Free):

Abstract

Artistic font design has become an integral part of visual media. However, without prior knowledge of the font domain, it is difficult to create distinct font styles. When the number of characters is limited, this task becomes easier (e.g., only Latin characters). However, designing CJK (Chinese, Japanese, and Korean) characters presents a challenge due to the large number of character sets and complexity of the glyph components in these languages. Numerous studies have been conducted on automating the font design process using generative adversarial networks (GANs). Existing methods rely heavily on reference fonts and perform font style conversions between different fonts. Additionally, rather than capturing style information for a target font via multiple style images, most methods do so via a single font image. In this paper, we propose a network architecture for generating multilingual font sets that makes use of geometric structures as content. Additionally, to acquire sufficient style information, we employ multiple style images belonging to a single font style simultaneously to extract global font style-specific information. By utilizing the geometric structural information of content and a few stylized images, our model can generate an entire font set while maintaining the style. Extensive experiments were conducted to demonstrate the proposed model's superiority over several baseline methods. Additionally, we conducted ablation studies to validate our proposed network architecture.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
S. Majeed, A. U. Hassan, J. Choi, "Few-Shot Content-Level Font Generation," KSII Transactions on Internet and Information Systems, vol. 16, no. 4, pp. 1166-1186, 2022. DOI: 10.3837/tiis.2022.04.005.

[ACM Style]
Saima Majeed, Ammar Ul Hassan, and Jaeyoung Choi. 2022. Few-Shot Content-Level Font Generation. KSII Transactions on Internet and Information Systems, 16, 4, (2022), 1166-1186. DOI: 10.3837/tiis.2022.04.005.

[BibTeX Style]
@article{tiis:25583, title="Few-Shot Content-Level Font Generation", author="Saima Majeed and Ammar Ul Hassan and Jaeyoung Choi and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.04.005}, volume={16}, number={4}, year="2022", month={April}, pages={1166-1186}}