Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges

TL;DR

This study explores how blind and low-vision users interact with object recognition systems, revealing their strategies, challenges, and preferences for error detection to inform more accessible assistive technology design.

Contribution

It provides empirical insights into user behaviors and challenges in error handling with object recognition systems for visually impaired users.

Findings

Users prefer independent error review but are apprehensive about misrecognitions.

Participants identified only half of the errors, with no significant improvement after repeated attempts.

Design implications for accessible interfaces are proposed based on user insights.

Abstract

Object recognition technologies hold the potential to support blind and low-vision people in navigating the world around them. However, the gap between benchmark performances and practical usability remains a significant challenge. This paper presents a study aimed at understanding blind users' interaction with object recognition systems for identifying and avoiding errors. Leveraging a pre-existing object recognition system, URCam, fine-tuned for our experiment, we conducted a user study involving 12 blind and low-vision participants. Through in-depth interviews and hands-on error identification tasks, we gained insights into users' experiences, challenges, and strategies for identifying errors in camera-based assistive technologies and object recognition systems. During interviews, many participants preferred independent error review, while expressing apprehension toward misrecognitions. In the error identification task, participants varied viewpoints, backgrounds, and object sizes in their images to avoid and overcome errors. Even after repeating the task, participants identified only half of the errors, and the proportion of errors identified did not significantly differ from their first attempts. Based on these insights, we offer implications for designing accessible interfaces tailored to the needs of blind and low-vision users in identifying object recognition errors.