Perceptions of Blind Adults on Non-Visual Mobile Text Entry

TL;DR

This paper investigates non-visual mobile text entry for blind and low-vision users by conducting 12 semi-structured interviews and evaluating an experimental prototype called FlexType. It identifies three core challenges: poor dictation accuracy, difficulty typing in noisy environments, and difficulty correcting errors, highlighting that many users still rely on dictation despite accuracy issues and are cautious about learning new methods. The authors distill five future research directions, including improving dictation, reducing dependence on audio feedback, enhancing error correction, lowering onboarding barriers for new methods, and enabling more fluid non-visual word predictions. The work provides user-centered guidance to improve accessibility and efficiency of mobile text input for BLV, with implications for future HCI and accessibility technologies. These insights can inform design choices and prioritize feature development in non-visual text input interfaces and assistive technologies.

Abstract

Text input on mobile devices without physical keys can be challenging for people who are blind or low-vision. We interview 12 blind adults about their experiences with current mobile text input to provide insights into what sorts of interface improvements may be the most beneficial. We identify three primary themes that were experiences or opinions shared by participants: the poor accuracy of dictation, difficulty entering text in noisy environments, and difficulty correcting errors in entered text. We also discuss an experimental non-visual text input method with each participant to solicit opinions on the method and probe their willingness to learn a novel method. We find that the largest concern was the time required to learn a new technique. We find that the majority of our participants do not use word predictions while typing but instead find it faster to finish typing words manually. Finally, we distill five future directions for non-visual text input: improved dictation, less reliance on or improved audio feedback, improved error correction, reducing the barrier to entry for new methods, and more fluid non-visual word predictions.