Glanceable, legible typography over complex backgrounds

Abstract

Modern digital interfaces display typeface in ways new to the 500 year old art of typography, driving a shift in reading from primarily long-form to increasingly short-form. In safety-critical settings, such at-a-glance reading competes with the need to understand the environment. To keep both type and the environment legible, a variety of ‘middle layer’ approaches are employed. But what is the best approach to presenting type over complex backgrounds so as to preserve legibility? This work tests and ranks middle layers in three studies. In the first study, Gaussian blur and semi-transparent ‘scrim’ middle layer techniques best maximise legibility. In the second, an optimal combination of the two is identified. In the third, letter-localised middle layers are tested, with results favouring drop-shadows. These results, discussed in mixed reality (MR) including overlays, virtual reality (VR), and augmented reality (AR), considers a future in which glanceable reading amidst complex backgrounds is common.

Practitioner summary: Typography over complex backgrounds, meant to be read and understood at a glance, was once niche but today is a growing design challenge for graphical user interface HCI. We provide a technique, evidence-based strategies, and illuminating results for maximising legibility of glanceable typography over complex backgrounds.

Abbreviations: AR: augmented reality; VR: virtual reality; HUD: head-up display; OLED: organic light-emitting diode; UX: user experience; MS: millisecond; CM: centimeter

Keywords:

• Perception
• vision and lighting
• environmental ergonomics
• information displays
• human-machine systems
• mixed reality virtual environments human-computer interaction

Acknowledgments

Support for this publication was provided through the Clear Information Presentation (Clear-IP) consortium at MIT. Clear-IP is a collaborative effort focussed on the study of legibility and related topics, primarily supported to date by Monotype Imaging and Google. Studies I and II were supported by Clear-IP and study III was independently supported by Monotype. The views and conclusions being expressed as those of the authors and may not necessarily represent those of individual sponsoring organisations.

Disclosure statement

Nadine Chahine was employed by Monotype at the time portions of this work were performed.

Ben D. Sawyer was employed by The Massachusetts Institute of Technology at the time portions of this work were performed.