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SaIL: Saliency-Driven Injection of ARIA Landmarks.

Ali Selman Aydin¹, Shirin Feiz¹, Vikas Ashok²

¹Stony Brook University.

IUI. International Conference on Intelligent User Interfaces

|February 15, 2021

Summary

This study introduces SaIL, an automated system that adds ARIA landmarks to webpages. SaIL improves screen reader navigation by identifying important content sections, reducing user time and effort.

Keywords:

WAI-ARIA landmarks screen reader web accessibility

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Area of Science:

Human-Computer Interaction
Web Accessibility
Artificial Intelligence

Background:

Screen reader navigation presents challenges despite advancements in technology and web standards like ARIA.
ARIA landmarks enable efficient webpage section access for screen reader users, but their implementation is inconsistent and often lacking.

Purpose of the Study:

To develop SaIL, a scalable approach for automatically detecting important webpage sections.
To inject ARIA landmarks into HTML markup to enhance screen reader navigation and accessibility.

Main Methods:

Utilizing visual saliency, determined by a deep learning model trained on user gaze-tracking data.
Developing an automated system to inject ARIA landmarks based on detected visual saliency.

Main Results:

A pilot study demonstrated SaIL's effectiveness.
SaIL has the potential to significantly reduce the time and effort required for screen reader users to navigate webpages.

Conclusions:

SaIL offers a promising solution to inconsistent ARIA landmark implementation.
Automated ARIA landmark injection can improve the web browsing experience for visually impaired users.