# Let Me Choose: From Verbal Context to Font Selection

arXiv: https://arxiv.org/abs/2005.01151 · HTML: https://mlsanity.com/paper/2005.01151/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 8 of 8)

- [Impressions2Font: Generating Fonts by Specifying Impressions](https://mlsanity.com/paper/2103.10036/) (2021)
- [Shared Latent Space of Font Shapes and Their Noisy Impressions](https://mlsanity.com/paper/2103.12347/) (2021)
- [Font Generation with Missing Impression Labels](https://mlsanity.com/paper/2203.10348/) (2022)
- [PostDoc: Generating Poster from a Long Multimodal Document Using Deep Submodular Optimization](https://mlsanity.com/paper/2405.20213/) (2024)
- [Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides](https://mlsanity.com/paper/2101.03237/) (2021)
- [What Text Design Characterizes Book Genres?](https://mlsanity.com/paper/2402.16356/) (2024)
- [Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualized Embeddings](https://mlsanity.com/paper/2101.11422/) (2021)
- [Font Impression Estimation in the Wild](https://mlsanity.com/paper/2402.15236/) (2024)

## Abstract (arXiv)

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.