Track: Browsers and User Interfaces
Paper Title:
CSurf: A Context-Driven Non-Visual Web-Browser
Authors:
Abstract:
Web sites are designed for graphical mode of interaction.
Sighted users can "cut to the chase" and quickly identify
relevant information in Web pages. On the contrary, indi-
viduals with visual disabilities have to use screen-readers to
browse the Web. As screen-readers process pages sequen-
tially and read through everything, Web browsing can be-
come strenuous and time-consuming. Although, the use of
shortcuts and searching offers some improvements, the prob-
lem still remains. In this paper, we address the problem
of information overload in non-visual Web access using the
notion of context. Our prototype system, CSurf, embodying
our approach, provides the usual features of a screen-reader.
However, when a user follows a link, CSurf captures the
context of the link using a simple topic-boundary detection
technique, and uses it to identify relevant information on
the next page with the help of a Support Vector Machine, a
statistical machine-learning model. Then, CSurf reads the
Web page starting from the most relevant section, identified
by the model. We conducted a series experiments to eval-
uate the performance of CSurf against the state-of-the-art
screen-reader, JAWS. Our results show that the use of con-
text can potentially save browsing time and substantially
improve browsing experience of visually disabled people.
