Interaction with web data records typically involves accessing auxiliary webpage segments such as filters, sort options, search form, and multi-page links. As these segments are usually scattered all across the screen, it is arduous and tedious for blind users who rely on screen readers to access the segments, given that content navigation with screen readers is predominantly one-dimensional, despite the available support for skipping content via either special keyboard shortcuts or selective navigation. The extant techniques to overcome inefficient web screen reader interaction have mostly focused on general web content navigation, and as such they provide little to no support for data record-specific interaction activities such as filtering and sorting – activities that are equally important for enabling quick and easy access to the desired data records. To fill this void, we present InSupport, a browser extension that: (i) employs custom-built machine learning models to automatically extract auxiliary segments on any webpage containing data records, and (ii) provides an instantly accessible proxy one-stop interface for easily navigating the extracted segments using basic screen reader shortcuts. An evaluation study with 14 blind participants showed significant improvement in usability with InSupport, driven by increased reduction in interaction time and the number ofmore »
SaIL: saliency-driven injection of ARIA landmarks
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.
- Award ID(s):
- 1805076
- Publication Date:
- NSF-PAR ID:
- 10186666
- Journal Name:
- 25th International Conference on Intelligent User Interfaces
- Page Range or eLocation-ID:
- 111 to 115
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3377325.3377540
