Few-shot Compositional Font Generation with Dual Memory

• URL: http://arxiv.org/abs/2005.10510v2
• Date: Thu, 16 Jul 2020 11:47:52 GMT
• Title: Few-shot Compositional Font Generation with Dual Memory
• Authors: Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee, Seonghyeon Kim, and Hwalsuk Lee
• Abstract summary: We propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font) We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization.
• Score: 16.967987801167514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have significant drawbacks; they require a large number of reference images to generate a new font set, or they fail to capture detailed styles with only a few samples. In this paper, we focus on compositional scripts, a widely used letter system in the world, where each glyph can be decomposed by several components. By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples. We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization compared to the state-of-the-art generation methods quantitatively and qualitatively. Source code is available at https://github.com/clovaai/dmfont.

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