A UCF readability researcher worked with an Adobe team on a machine learning model to provide personalized font recommendations that improve the accessibility of digital information and enhance individual reading experiences.

Figure 1: A machine learning model to provide personalized font.

Figure 1 shows thatthe team was comprised of Adobe machine learning engineers and researchers who collaborated with vision scientists, typographers, data scientists, and a UCF readability researcher to study Adobe’s machine learning model known as FontMART. [1]

Adobe is part of The Readability Consortium, which is leading research on the readability of UCF’s digital texts using custom typography to improve the readability of digital texts for readers of all ages and abilities. The Adobe FontMART study was done in collaboration with the UCF Virtual Readability Lab.

Considering font preferences is important, as the fonts that people prefer are often different from the font that can best improve their reading experience and productivity.The FontMART model learns to associate fonts with specific reader characteristics.

FontMART was trained with a remote readability study of 252 crowdsources and self-reported demographic information. Interviews with typographers influenced the selection of the eight typefaces used in the study. The final font selection included fonts from both serif families and sans serifs. [2]

FontMART can predict the fonts that work nicely for particular readers by understanding the connection between font traits and reader traits like font familiarity, self-reported studying pace and age, in keeping with the FontMART research. Among the many traits thought of, age performs the most important position when the mannequin determines which font is really helpful for readers.

Continued collaborations and analysis will assist develop the traits explored to enhance the FontMART mannequin and improve particular person studying experiences.UCF’s Readability Consortium and Digital Readability Lab tackle how personalization can enhance studying effectivity and pace.

Sawyer additionally leads LabX, a utilized neuroscience group centered on human performance, and he’s an affiliate professor in industrial engineering and administration programs. Sawyer acquired a doctorate in human elements psychology and a grasp’s diploma in industrial engineering from UCF. He accomplished his postdoctoral research at MIT. [3]

References:

1. https://upjobsnews.com/ai-model-recommends-personalized-fonts-to-improve-digital-reading-accessibility-more-latest-news-here/
2. https://newjerseyupdates.com/the-ai-%E2%80%8B%E2%80%8Bmodel-recommends-personalized-fonts-to-improve-digital-readability-and-accessibility/80959/
3. https://news8plus.com/ai-model-recommends-personalized-fonts-to-improve-digital-reading-accessibility/

Cite this article:

Thanusri swetha J (2022), AI’s Personalized Fonts to Improve Digital Reading, Anatechmaz, pp.169