Font Representation Learning via Paired-glyph Matching

TL;DR

This paper introduces a novel font representation learning method using paired-glyph matching, which improves font retrieval and style transfer by embedding font styles into a discriminative latent space.

Contribution

The proposed scheme effectively learns high-quality font representations by attracting glyphs of the same font and repelling those of different fonts, enhancing font retrieval and style transfer.

Findings

Outperforms existing font representation techniques in retrieval tasks

Improves generalization in font retrieval with new fonts

Enhances font style transfer and generation through transfer learning

Abstract

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.