Web data records are usually accompanied by auxiliary webpage segments, such as filters, sort options, search form, and multi-page links, to enhance interaction efficiency and convenience for end users. However, blind and visually impaired (BVI) persons are presently unable to fully exploit the auxiliary segments like their sighted peers, since these segments are scattered all across the screen, and as such assistive technologies used by BVI users, i.e., screen reader and screen magnifier, are not geared for efficient interaction with such scattered content. Specifically, for blind screen reader users, content navigation is predominantly one-dimensional despite the support for skipping content, and therefore navigating to-and-fro between different parts of the webpage is tedious and frustrating. Similarly, low vision screen magnifier users have to continuously pan back-and-forth between different portions of a webpage, given that only a portion of the screen is viewable at any instant due to content enlargement. The extant techniques to overcome inefficient web interaction for BVI users have mostly focused on general web-browsing activities, 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 facilitating quick and easy access to desired data records. To fill this void, we present InSupport, a browser extension that: (i) employs custom machine learning-based algorithms 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 auxiliary segments using either basic keyboard shortcuts or mouse actions. Evaluation studies with 14 blind participants and 16 low vision participants showed significant improvement in web usability with InSupport, driven by increased reduction in interaction time and user effort, compared to the state-of-the-art solutions.
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AutoDesc: Facilitating Convenient Perusal of Web Data Items for Blind Users
Web data items such as shopping products, classifieds, and job listings are indispensable components of most e-commerce websites. The information on the data items are typically distributed over two or more webpages, e.g., a ‘Query-Results’ page showing the summaries of the items, and ‘Details’ pages containing full information about the items. While this organization of data mitigates information overload and visual cluttering for sighted users, it however increases the interaction overhead and effort for blind users, as back-and-forth navigation between webpages using screen reader assistive technology is tedious and cumbersome. Existing usability-enhancing solutions are unable to provide adequate support in this regard as they predominantly focus on enabling efficient content access within a single webpage, and as such are not tailored for content distributed across multiple webpages. As an initial step towards addressing this issue, we developed AutoDesc, a browser extension that leverages a custom extraction model to automatically detect and pull out additional item descriptions from the ‘details’ pages, and then proactively inject the extracted information into the ‘Query-Results’ page, thereby reducing the amount of back-and-forth screen reader navigation between the two webpages. In a study with 16 blind users, we observed that within the same time duration, the participants were able to peruse significantly more data items on average with AutoDesc, compared to that with their preferred screen readers as well as with a state-of-the-art solution.
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- Award ID(s):
- 2045523
- NSF-PAR ID:
- 10403599
- Date Published:
- Journal Name:
- Proceedings of the 28th International Conference on Intelligent User Interfaces
- Page Range / eLocation ID:
- 32 to 45
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3581641.3584049
