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Digital reading applications give readers the ability to customize fonts,sizes, and spacings, all of which have been shown to improve the readingexperience for readers from different demographics. However, tweaking thesetext features can be challenging, especially given their interactions on thefinal look and feel of the text. Our solution is to offer readers presetcombinations of font, character, word and line spacing, which we bundletogether into reading themes. To arrive at a recommended set of reading themes,we present our THERIF pipeline, which combines crowdsourced text adjustments,ML-driven clustering of text formats, and design sessions. We show that afterfour iterations of our pipeline, we converge on a set of three COR themes(Compact, Open, and Relaxed) that meet diverse readers' preferences, whenevaluating the reading speeds, comprehension scores, and preferences ofhundreds of readers with and without dyslexia, using crowdsourced experiments.

THERIF: A Pipeline for Generating Themes for Readability with Iterative Feedback