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

TL;DR

AdaptiFont is a human-in-the-loop system that uses generative font modeling and Bayesian optimization to personalize and enhance reading speed for individual users on digital screens.

Contribution

It introduces a novel adaptive font generation method combining generative modeling and Bayesian optimization to improve individual reading speed.

Findings

Fonts significantly increased reading speed.

Fonts differ notably across individuals.

System effectively identifies high-speed fonts for users.

Abstract

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.