  Acquiring the desired font for various design tasks can be challenging and
requires professional typographic knowledge. While previous font retrieval or
generation works have alleviated some of these difficulties, they often lack
support for multiple languages and semantic attributes beyond the training data
domains. To solve this problem, we present FontCLIP: a model that connects the
semantic understanding of a large vision-language model with typographical
knowledge. We integrate typography-specific knowledge into the comprehensive
vision-language knowledge of a pretrained CLIP model through a novel finetuning
approach. We propose to use a compound descriptive prompt that encapsulates
adaptively sampled attributes from a font attribute dataset focusing on Roman
alphabet characters. FontCLIP's semantic typographic latent space demonstrates
two unprecedented generalization abilities. First, FontCLIP generalizes to
different languages including Chinese, Japanese, and Korean (CJK), capturing
the typographical features of fonts across different languages, even though it
was only finetuned using fonts of Roman characters. Second, FontCLIP can
recognize the semantic attributes that are not presented in the training data.
FontCLIP's dual-modality and generalization abilities enable multilingual and
cross-lingual font retrieval and letter shape optimization, reducing the burden
of obtaining desired fonts.
