ImageAssist: Tools for Enhancing Touchscreen-Based Image Exploration Systems for Blind and Low Vision Users

Abstract

Blind and low vision (BLV) users often rely on alt text to understand what a digital image is showing. However, recent research has investigated how touch-based image exploration on touchscreens can supplement alt text. Touchscreen-based image exploration systems allow BLV users to deeply understand images while granting a strong sense of agency. Yet, prior work has found that these systems require a lot of effort to use, and little work has been done to explore these systems' bottlenecks on a deeper level and propose solutions to these issues. To address this, we present ImageAssist, a set of three tools that assist BLV users through the process of exploring images by touch -- scaffolding the exploration process. We perform a series of studies with BLV users to design and evaluate ImageAssist, and our findings reveal several implications for image exploration tools for BLV users.

References in corpus (6)

• Deep Residual Learning for Image Recognition
• Improved Image Captioning via Policy Gradient optimization of SPIDEr
• Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
• Accessible Interactive Maps for Visually Impaired Users
• NavStick: Making Video Games Blind-Accessible via the Ability to Look Around
• Uncovering Visually Impaired Gamers' Preferences for Spatial Awareness Tools Within Video Games

Cited by in corpus (8)

• Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People
• "It's Kind of Context Dependent": Understanding Blind and Low Vision People's Video Accessibility Preferences Across Viewing Scenarios
• Context-Aware Image Descriptions for Web Accessibility
• TADA: Making Node-link Diagrams Accessible to Blind and Low-Vision People
• Surveyor: Facilitating Discovery Within Video Games for Blind and Low Vision Players
• Engaging with Children's Artwork in Mixed Visual-Ability Families
• Branch Explorer: Leveraging Branching Narratives to Support Interactive 360° Video Viewing for Blind and Low Vision Users
• Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions