Improving Low-Vision Chart Accessibility via On-Cursor Visual Context

2026-03-02

AI Summary

This study addresses the challenges low-vision users face when exploring data visualizations, particularly due to restricted field of view and reliance on magnification, which impede access to global contextual information. To mitigate this, the authors propose two novel interaction techniques: Dynamic Context, which dynamically reveals focal content alongside relevant contextual elements, and Mini-map, a vision-adapted overview-plus-detail interface. Both approaches are grounded in four key types of visual context identified through user research—axes, legends, gridlines, and overall chart layout—and employ pointer-driven mechanisms for real-time contextual display. An evaluation with 22 low-vision participants demonstrated that Dynamic Context significantly improves accessibility and usability while reducing interaction overhead, whereas Mini-map enhances spatial understanding despite lower user preference.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Interaction Techniques and Devices
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Despite widespread use, charts remain largely inaccessible for Low-Vision Individuals (LVI). Reading charts requires viewing data points within a global context, which is difficult for LVI who may rely on magnification or experience a partial field of vision. We aim to improve exploration by providing visual access to critical context. To inform this, we conducted a formative study with five LVI. We identified four fundamental contextual elements common across chart types: axes, legend, grid lines, and the overview. We propose two pointer-based interaction methods to provide this context: Dynamic Context, a novel focus+context interaction, and Mini-map, which adapts overview+detail principles for LVI. In a study with N=22 LVI, we compared both methods and evaluated their integration to current tools. Our results show that Dynamic Context had significant positive impact on access, usability, and effort reduction; however, worsened visual load. Mini-map strengthened spatial understanding, but was less preferred for this task. We offer design insights to guide the development of future systems that support LVI with visual context while balancing visual load.

Problem

• Low-Vision Accessibility
• Data Visualization
• Visual Context
• Chart Reading
• Human-Computer Interaction

Innovation

• Dynamic Context
• Mini-map
• Low-Vision Accessibility
• Focus+Context Interaction
• Visual Context