Few-Shot Font Generation with Deep Metric Learning

• URL: http://arxiv.org/abs/2011.02206v1
• Date: Wed, 4 Nov 2020 10:12:10 GMT
• Title: Few-Shot Font Generation with Deep Metric Learning
• Authors: Haruka Aoki, Koki Tsubota, Hikaru Ikuta, Kiyoharu Aizawa
• Abstract summary: The proposed framework introduces deep metric learning to style encoders. We performed experiments using black-and-white and shape-distinctive font datasets.
• Score: 33.12829580813688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing fonts for languages with a large number of characters, such as Japanese and Chinese, is an extremely labor-intensive and time-consuming task. In this study, we addressed the problem of automatically generating Japanese typographic fonts from only a few font samples, where the synthesized glyphs are expected to have coherent characteristics, such as skeletons, contours, and serifs. Existing methods often fail to generate fine glyph images when the number of style reference glyphs is extremely limited. Herein, we proposed a simple but powerful framework for extracting better style features. This framework introduces deep metric learning to style encoders. We performed experiments using black-and-white and shape-distinctive font datasets and demonstrated the effectiveness of the proposed framework.

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