CSurf: A Context-Driven Non-Visual Web-Browser

摘要

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, individuals with visual disabilities have to use screen-readers to browse the Web. As screen-readers process pages sequentially and read through everything, Web browsing can become strenuous and time-consuming. Although, the use of shortcuts and searching offers some improvements, the problem 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 evaluate the performance of CSurf against the state-of-the-art screen-reader, JAWS. Our results show that the use of context can potentially save browsing time and substantially improve browsing experience of visually disabled people.