Fonts Like This but Happier: A New Way to Discover Fonts

Fonts carry strong emotional and social signals, and can affect user engagement in significant ways. Hence, selecting the right font is a very important step in the design of a multimodal artifact with text. Currently, font exploration is frequently carried out via associated social tags. Users are expected to browse through thousands of fonts tagged with certain concepts to find the one that works best for their use case. In this study, we propose a new multimodal font discovery method in which users provide a reference font together with the changes they wish to obtain in order to get closer to their ideal font. This allows for efficient and goal-driven navigation of the font space, and discovery of fonts that would otherwise likely be missed. We achieve this by learning cross-modal vector representations that connect fonts and query words.