Let Me Choose: From Verbal Context to Font Selection
This work addresses font selection for designers and marketers in applications where text is the only visual element, but it is incremental as it builds on existing methods for visual-text associations.
The paper tackled the problem of learning associations between verbal context and font selection by introducing a new dataset of social media posts and ads, and investigating end-to-end models to handle subjective label distributions, achieving results that capture inter-subjectivity across annotations.
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.