Document Type

Conference Paper

Publication Date

2020

DOI

10.1145/3377325.3377540

Publication Title

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

Pages

111-115

Conference Name

IUI ’20: International Conference on Intelligent User Interfaces, March 2020, Cagliari, Italy

Abstract

Navigating webpages with screen readers is a challenge even with recent improvements in screen reader technologies and the increased adoption of web standards for accessibility, namely ARIA. ARIA landmarks, an important aspect of ARIA, lets screen reader users access different sections of the webpage quickly, by enabling them to skip over blocks of irrelevant or redundant content. However, these landmarks are sporadically and inconsistently used by web developers, and in many cases, even absent in numerous web pages. Therefore, we propose SaIL, a scalable approach that automatically detects the important sections of a web page, and then injects ARIA landmarks into the corresponding HTML markup to facilitate quick access to these sections. The central concept underlying SaIL is visual saliency, which is determined using a state-of-the-art deep learning model that was trained on gaze-tracking data collected from sighted users in the context of web browsing. We present the findings of a pilot study that demonstrated the potential of SaIL in reducing both the time and effort spent in navigating webpages with screen readers.

Comments

© 2020 ACM.

Included with permission of the publisher by green open access policy.

Publisher's version available at: https://doi.org/10.1145/3377325.3377540

Original Publication Citation

Aydin, A. S., Feiz, S., Ashok, V., & Ramakrishnan, I. V. (2020). SaIL: Saliency-driven injection of ARIA landmarks. IUI ’20: International Conference on Intelligent User Interfaces, March 2020, Cagliari, Italy. https://doi.org/10.1145/3377325.3377540

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