document: Csurf : a context-driven non-visual web-browser 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 tobrowse the Web . As screen-readers process pages sequentially and read through everything , Web browsing can become strenuous and time-consuming . Although , the use ofshortcuts and searching offers some improvements , the problem still remains . In this paper , we address the problemof information overload in non-visual Web access using thenotion of context . Our prototype system , CSurf , embodyingor approach , provides the usual features of a screen-reader . However , when a user follows a link , CSurf captures thecontext of the link using a simple topic-boundary detectiontechnique , and uses it to identify relevant information onthe next page with the help of a Support Vector Machine , astatistical machine-learning model . Then , CSurf reads the Web page starting from the most relevant section , identifiedby the model . We conducted a series experiments to evaluate the performance of CSurf against the state-of-the-artscreen-reader , JAWS . Our results show that the use of context can potentially save browsing time and substantiallyimprove browsing experience of visually disabled people .
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