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.

翻译：数字文本已成为交流知识的主要方法之一, 但文本需要被转换为屏幕阅读 。 我们展示了 SandaniFont, 这是一种人行环形系统, 旨在交互增加显示在显示器上显示的文字的可读性。 为此, 我们首先从一组经典字体中学习一个非负矩阵化的基因字体空间 。 在这个空间中, 我们通过积极学习生成新的真型字体, 以新字体制作文本, 并测量个人用户的阅读速度 。 巴伊西亚优化按顺序在飞行上生成新的字体, 以逐步提高个人的阅读速度 。 用户研究的结果显示, 这个适应性字体生成系统在字体空间找到与阅读速度相当的区域, 这些字体极大地提高了参与者的阅读速度, 发现字体在不同的读者中有很大差异 。