# AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization

arXiv: https://arxiv.org/abs/2104.10741 · HTML: https://mlsanity.com/paper/2104.10741/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 90 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)
- [STEFANN: Scene Text Editor using Font Adaptive Neural Network](https://mlsanity.com/paper/1903.01192/) (2019)
- [Few-shot Font Generation with Localized Style Representations and Factorization](https://mlsanity.com/paper/2009.11042/) (2020)
- [Few-shot Compositional Font Generation with Dual Memory](https://mlsanity.com/paper/2005.10510/) (2020)
- [Large-scale Tag-based Font Retrieval with Generative Feature Learning](https://mlsanity.com/paper/1909.02072/) (2019)
- [Automatic Playtesting for Game Parameter Tuning via Active Learning](https://mlsanity.com/paper/1908.01417/) (2019)
- [Optimizing User Interface Layouts via Gradient Descent](https://mlsanity.com/paper/2002.10702/) (2020)
- [The Automatic Neuroscientist: automated experimental design with real-time fMRI](https://mlsanity.com/paper/1506.02088/) (2015)
- [Computational Design with Crowds](https://mlsanity.com/paper/2002.08657/) (2020)

## Built on by (most cited first, 5 of 5)

- [Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques](https://mlsanity.com/paper/2204.07641/) (2022)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [Contextual Font Recommendations based on User Intent](https://mlsanity.com/paper/2306.08188/) (2023)
- [GRAPHIC--Guidelines for Reviewing Algorithmic Practices in Human-centred Design and Interaction for Creativity](https://mlsanity.com/paper/2511.17443/) (2025)
- [GimmBO: Interactive Generative Image Model Merging via Bayesian Optimization](https://mlsanity.com/paper/2601.18585/) (2026)

## Abstract (arXiv)

Digital text has become one of the primary ways of exchanging knowledge, but text needs to be rendered to a screen to be read. We present AdaptiFont, a human-in-the-loop system that is aimed at interactively increasing readability of text displayed on a monitor. To this end, we first learn a generative font space with non-negative matrix factorization from a set of classic fonts. In this space we generate new true-type-fonts through active learning, render texts with the new font, and measure individual users' reading speed. Bayesian optimization sequentially generates new fonts on the fly to progressively increase individuals' reading speed. The results of a user study show that this adaptive font generation system finds regions in the font space corresponding to high reading speeds, that these fonts significantly increase participants' reading speed, and that the found fonts are significantly different across individual readers.