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

要旨

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.

著者
Florian Kadner
Technical University Darmstadt, Darmstadt, Hesse, Germany
Yannik Keller
Technical University Darmstadt, Darmstadt, Hesse, Germany
Constantin Rothkopf
Technical University of Darmstadt, Darmstadt, Germany
DOI

10.1145/3411764.3445140

論文URL

https://doi.org/10.1145/3411764.3445140

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Computational Design

[A] Paper Room 02, 2021-05-12 17:00:00~2021-05-12 19:00:00 / [B] Paper Room 02, 2021-05-13 01:00:00~2021-05-13 03:00:00 / [C] Paper Room 02, 2021-05-13 09:00:00~2021-05-13 11:00:00
Paper Room 02
15 件の発表
2021-05-12 17:00:00
2021-05-12 19:00:00
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