# Serif or Sans: Visual Font Analytics on Book Covers and Online Advertisements

arXiv: https://arxiv.org/abs/1906.10269 · HTML: https://mlsanity.com/paper/1906.10269/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Shared Latent Space of Font Shapes and Their Noisy Impressions](https://mlsanity.com/paper/2103.12347/) (2021)
- [Which Parts Determine the Impression of the Font?](https://mlsanity.com/paper/2103.14216/) (2021)
- [Analyzing Font Style Usage and Contextual Factors in Real Images](https://mlsanity.com/paper/2306.12050/) (2023)
- [What Text Design Characterizes Book Genres?](https://mlsanity.com/paper/2402.16356/) (2024)
- [Reimagining Application User Interface (UI) Design using Deep Learning Methods: Challenges and Opportunities](https://mlsanity.com/paper/2303.13055/) (2023)
- [Font Impression Estimation in the Wild](https://mlsanity.com/paper/2402.15236/) (2024)
- [ColorGPT: Leveraging Large Language Models for Multimodal Color Recommendation](https://mlsanity.com/paper/2508.08987/) (2025)

## Abstract (arXiv)

In this paper, we conduct a large-scale study of font statistics in book covers and online advertisements. Through the statistical study, we try to understand how graphic designers relate fonts and content genres and identify the relationship between font styles, colors, and genres. We propose an automatic approach to extract font information from graphic designs by applying a sequence of character detection, style classification, and clustering techniques to the graphic designs. The extracted font information is accumulated together with genre information, such as romance or business, for further trend analysis. Through our unique empirical study, we show that the collected font statistics reveal interesting trends in terms of how typographic design represents the impression and the atmosphere of the content genres.