Semantics-aware typographical choices via affective associations

Abstract

With the tens of thousands of fonts that are now readily available, it is non-trivial to select the most suitable font for a given use case. Considering the impact of the choice of font on human perception of the text, there is a strong need for semantic font search and recommendation. Aiming to fulfill this need, we induce a typographical lexicon providing associations between words and fonts. For this purpose, we determine font vectors for basic and complex emotions, based on word similarities, antonymy information, and Plutchik’s Wheel of Emotions. We create a large font lexicon, named FontLex, relying on emotion associations between the words and the fonts. We evaluate our results through user studies and find that for the majority of the evaluated words, the fonts recommended by FontLex are preferred. We also further extend the dataset using synonyms of font attributes and emotion names. Finally, using CNN embeddings of the fonts, we expand our attribute score assignment to new fonts. The resulting FontLex resource provides mappings between 6.7K words and 2K fonts. Our proof of concept application demonstrates how FontLex can be invoked to obtain semantic font recommendation for poster design.