mlsanityData to 7 Oct 2026

Font Representation Learning via Paired-glyph Matching

arXiv 2211.10967 ↗First posted 20 Nov 2022cs.CVcited by 0

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Abstract (arXiv)

Fonts can convey profound meanings of words in various forms of glyphs. Without typography knowledge, manually selecting an appropriate font or designing a new font is a tedious and painful task. To allow users to explore vast font styles and create new font styles, font retrieval and font style transfer methods have been proposed. These tasks increase the need for learning high-quality font representations. Therefore, we propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model that attracts the representations of glyphs in the same font to one another, but pushes away those of other fonts. Through evaluations on font retrieval with query glyphs on new fonts, we show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques. Finally on the downstream font style transfer and generation tasks, we confirm the benefits of transfer learning with the proposed method. The source code is available at https://github.com/junhocho/paired-glyph-matching.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 20 Nov 2022.

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21 of 43 references.

References · 21

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9 more
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