Modeling Fonts in Context: Font Prediction on Web Designs

Abstract

Web designers often carefully select fonts to fit the context of a web design to make the design look aesthetically pleasing and effective in communication. However, selecting proper fonts for a web design is a tedious and time-consuming task, as each font has many properties, such as font face, color, and size, resulting in a very large search space. In this paper, we aim to model fonts in context, by studying a novel and challenging problem of predicting fonts that match a given web design. To this end, we propose a novel, multi-task deep neural network to jointly predict font face, color and size for each text element on a web design, by considering multi-scale visual features and semantic tags of the web design. To train our model, we have collected a CTXFont dataset, which consists of 1k professional web designs, with labeled font properties. Experiments show that our model outperforms the baseline methods, achieving promising qualitative and quantitative results on the font selection task. We also demonstrate the usefulness of our method in a font selection task via a user study.