TableNarrator: Making Image Tables Accessible to Blind and Low Vision People

要旨

The widespread use of image tables presents significant accessibility challenges for blind and low vision (BLV) people, limiting their access to critical data. Despite advancements in artificial intelligence (AI) for interpreting image tables, current solutions often fail to consider the specific needs of BLV users, leading to a poor user experience. To address these issues, we introduce TableNarrator, an innovative system designed to enhance the accessibility of image tables. Informed by accessibility standards and user feedback, TableNarrator leverages AI to generate alternative text tailored to the cognitive and reading preferences of BLV users. It streamlines access through a simple interaction mode and offers personalized options. Our evaluations, from both technical and user perspectives, demonstrate that TableNarrator not only provides accurate and comprehensive table information but also significantly enhances the user experience for BLV people.

著者
Ye Mo
Zhejiang University, Hangzhou, Zhejiang, China
Gang Huang
Zhejiang University, Hangzhou, China
liangcheng li
Zhejiang University, Hangzhou, Zhejiang Province, China
Dazhen Deng
Zhejiang University, Ningbo, Zhejiang, China
Zhi Yu
Zhejiang University, Hangzhou, China
Yilun Xu
Zhejiang University, Hangzhou, China
Kai Ye
Zhejiang University, Ningbo, Zhejiang, China
Sheng Zhou
School of Software Technology, Ningbo, Zhejiang, China
Jiajun Bu
Zhejiang University, Hangzhou, Zhejiang, China
DOI

10.1145/3706598.3714329

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714329

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Designs for Blind and Low Vision People

G303
6 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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