A feasibility study on filtering low-accessibility web pages considering color vision deficiency

2026-06-20

AI Summary

This study addresses the challenge of color accessibility for users with color vision deficiencies when browsing the web by proposing a machine learning–based automatic filtering approach. It pioneers the use of predictive modeling to identify and exclude webpages that violate Color Universal Design (CUD) principles, thereby exhibiting low accessibility. The method integrates CUD guidelines into a tailored evaluation metric, and experiments on 21 real-world webpages demonstrate that the model achieves a maximum AUC of 0.76, confirming the feasibility of automatically enhancing web color accessibility. This work offers a scalable technical pathway and practical insights for improving information accessibility for individuals with color vision deficiencies.

Abstract

Recently, the importance of universal design has increased. Color universal design (CUD) is one type of universal design that takes people with color vision deficiency (CVD) into consideration. Websites are important media for providing various types of information and functions. Therefore, it is essential to enhance the accessibility of web pages by incorporating CUD principles. The goal of our study is to help improve the accessibility of web pages. Our approach is to automatically filter low-accessibility web pages. To evaluate the feasibility of this approach, we conducted an experiment using 21 web pages. The prediction model identified low-accessibility pages with reasonable accuracy, achieving a maximum AUC of 0.76.

Problem

• color vision deficiency
• web accessibility
• color universal design
• low-accessibility web pages
• universal design

Innovation

• color universal design
• color vision deficiency
• web accessibility
• automatic filtering
• prediction model