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

arXiv: https://arxiv.org/abs/2302.09124 · HTML: https://mlsanity.com/paper/2302.09124/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2023.

## Built on by (most cited first, 6 of 6)

- [Investigating Use Cases of AI-Powered Scene Description Applications for Blind and Low Vision People](https://mlsanity.com/paper/2403.15604/) (2024)
- [Surfacing Variations to Calibrate Perceived Reliability of MLLM-generated Image Descriptions](https://mlsanity.com/paper/2507.15692/) (2025)
- [SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images](https://mlsanity.com/paper/2407.09686/) (2024)
- [Engaging with Children's Artwork in Mixed Visual-Ability Families](https://mlsanity.com/paper/2407.18874/) (2024)
- [How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People](https://mlsanity.com/paper/2602.13469/) (2026)
- [SweeperBot: Making 3D Browsing Accessible through View Analysis and Visual Question Answering](https://mlsanity.com/paper/2511.14567/) (2025)

## Abstract (arXiv)

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.