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.

现在有了成千上万种字体，为给定用例选择最合适的字体并非易事。考虑到字体选择对人类对文本的感知的影响，非常需要语义字体搜索和推荐。为了满足这种需求，我们引入了一个印刷词典，提供单词和字体之间的关联。为此，我们基于单词相似性，反义信息和Plutchik的情感之轮，确定基本和复杂情感的字体矢量。我们依赖于单词和字体之间的情感关联来创建一个名为FontLex的大型字体词典。我们通过用户研究评估结果，发现对于大多数评估词，FontLex推荐的字体是首选。我们还使用字体属性和情感名称的同义词进一步扩展了数据集。最后，使用字体的CNN嵌入，我们将属性得分分配扩展到新字体。生成的FontLex资源提供6.7K字和2K字体之间的映射。我们的概念证明应用程序演示了如何调用FontLex以获得海报设计的语义字体推荐。