  In this paper, we aim to learn associations between visual attributes of
fonts and the verbal context of the texts they are typically applied to.
Compared to related work leveraging the surrounding visual context, we choose
to focus only on the input text as this can enable new applications for which
the text is the only visual element in the document. We introduce a new
dataset, containing examples of different topics in social media posts and ads,
labeled through crowd-sourcing. Due to the subjective nature of the task,
multiple fonts might be perceived as acceptable for an input text, which makes
this problem challenging. To this end, we investigate different end-to-end
models to learn label distributions on crowd-sourced data and capture
inter-subjectivity across all annotations.
