Assessment of newly designed fonts for visual accessibility

Gordon E Legge, Yingzi Xiong, Qingying Gao, Rachel Gage, Taylor Knickel and Charles Bigelow

PLOS ONE, 2026, vol. 21, issue 3, 1-16

Abstract: Purpose: The visual accessibility of fonts refers to the range of print sizes and efficiency with which readers can access text. One goal of font design may be to maximize accessibility for a wide range of users including those with low vision. Here, we compare behavioral and automated methods for evaluating the accessibility of a font for both normal and simulated low vision. Method: We evaluated the accessibility of a newly designed font, ACT Easy. In Experiment 1, we used a behavioral (psychophysical) approach to compare regular and bold versions of ACT Easy to Courier, Frutiger, and Gotham. 22 normally sighted young adults were tested with a computerized version of MNREAD in two conditions: normal viewing, and text digitally filtered to simulate moderate low vision (20/90 acuity). The outcome measures were reading acuity, critical print size, maximum reading speed, and participants’ preference rankings. In Experiment 2, we used an automated method to estimate the equivalent of reading acuity for eleven state-of-the-art Optical Character Recognition models. The models read MNREAD sentences in ACT Easy and five mainstream fonts. We explored how accurately the models mimicked human performance. Results: In Experiment 1, ACT Easy Regular compared well in reading acuity and critical print size with Courier, the best of the other fonts for both normal and simulated low-vision conditions. ACT Easy Regular and Gotham were favored in the preference rankings. In Experiment 2, nine of the eleven OCR models showed changes in reading acuity similar to humans in the normal and simulated low-vision conditions. Two of the models also exhibited human-like variations across fonts. Conclusions: Behavioral and automated methods are both capable of revealing subtle differences in the visual accessibility of fonts. The behavioral method requires labor-intensive human testing. The automated method does not require human testing, and may sometimes provide a practical alternative.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0345068

DOI: 10.1371/journal.pone.0345068

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