Font Representation Learning via Paired-glyph Matching
This addresses the tedious task of font selection and design for users without typography knowledge, though it appears incremental as it builds on existing font representation learning techniques.
The paper tackles the problem of learning high-quality font representations for font retrieval and style transfer by proposing a paired-glyph matching model that clusters glyphs from the same font while separating those from different fonts. The result shows better generalization performance in font retrieval with new fonts and benefits in downstream tasks like font style transfer and generation.
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.