Fonts are complex. Even a single font like Garamond has many different flavors created by different foundries for the same font size.

Finding your fastest font is personal. Each of the 352 participants did the reading experiment with 5 fonts. Noto and Times fonts was seen by every participant along with 3 other fonts selected at random. Each line in this chart shows their reading speed range from the slowest to the fastest speed. On top of each line, colored dots show the speeds for the Noto (yellow) and Times (blue) fonts. Grey dots represent the other 3 fonts a participant tried and a black dot highlights their preferred font (amongst the 5 font options).

Each line in this chart shows a participant reading speed range from the slowest to the fastest speed. The black lines highlight if the font was the fastest for that participant. There is no pattern in the black highlights because there is no one fastest font that works for everyone.

Readability Visualizations

Communicating the Readability Consortium’s dramatic research findings with data visualizations, presentations and papers.


Finding your fastest font is personal

The “Towards Individuated Reading Experiences” study demonstrated that just changing the font used for reading can dramatically change reading speeds. One of the participants in this study jumped their reading speed by 239 words per minutes. The key finding here was that there is no discernible pattern to which font performs best. There is no fastest font that works for everyone. Created the visualization that shows the speed jumps each of the 352 participants along with the fonts they were testing. The visualization shows the jumps and the lack of any pattern for any one font. Building this chart would have been tedious if not impossible with conventional charting tools but Project Lincoln made it very quick and easy to construct and iterate on with the research team.

Papers

Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals

ACM Transactions on Computer-Human Interaction Volume 29 Issue 4 August 2022

Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability

DIS '22

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

CHI ‘23

COR Themes for Readability from Iterative Feedback

CHI '24 (Honorable mention award)

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