THERIF: Themes for Readability from Iterative Feedback

Abstract

Digital reading applications give readers the ability to customize fonts, sizes, and spacings, all of which have been shown to improve the reading experience for readers from different demographics. However, tweaking these text features can be challenging, especially given their interactions on the final look and feel of the text. Our solution is to offer readers preset combinations of font, character, word and line spacing, which we bundle together into reading themes. To arrive at a recommended set of reading themes, we combine crowdsourced text adjustments, ML-driven clustering of the resulting text formats, and design sessions. After four iterations of these steps, we converge on a set of three COR (Compact, Open, and Relaxed) themes that are designed for different readers.