For many users with a physical or motor disability, using a computer mouse or other pointing device to navigate the web is cumbersome or impossible due to problems with pointing accuracy. At the same time, web accessibility using a keyboard in major browsers is rudimentary, requiring many key presses to select links or other elements. We introduce KeySurf, a character controlled web navigation system which addresses this situation by presenting an interface which allows a user to activate any web page element with only two or three keystrokes. Through an implementation of a user-centric incremental search algorithm, elements are matched according to user expectation as characters are entered into the interface. We show how our interface can be integrated with a speech recognition input, as well as with specialized on-screen keyboards for people with disabilities. Using the user's browsing history, we improve the efficiency of the selection process and find potentially interesting page links for the user within the current web page. We present the results from a pilot study evaluating the performance of various components of our system.

For many users with a disability it can be difficult or impossible to use a computer mouse to navigate the web. An alternative way to select elements on a web page is the label typing approach, in which users select elements by typing part of the label. In most cases, these labels are specified by the page authors, but some selectable elements do not have an obvious textual description, thus requiring that a label be generated. The set of element labels on a web page must be both efficient to select by text input and meaningful to the user. This paper discusses our approach to this problem, using page structural analysis and user history to determine important elements of a page, and then matching this information with the efficiency model of the input device.