Bridging the Web Accessibility Divide

The Web has become the primary medium for accessing information and for conducting many types of online transactions, including shopping, paying bills, making travel plans, etc. The primary mode of interaction over the Web is via graphical browsers designed for visual navigation. Sighted users can visually segment web pages and quickly identify relevant information. On the contrary, screen readers- the dominant assistive technology used by visually impaired individuals- function by speaking out the screen’s content serially. Consequently, users with visual impairments are forced to listen to the information in web pages sequentially, thereby experiencing considerable information overload. This problem becomes even more prominent when conducting online transactions that often involve a number of steps spanning several pages. Thus, there is a large gap in Web accessibility between individuals with visual impairments and their sighted counterparts. This paper we describe our ongoing work on this problem. We have developed several techniques that synergistically couple web content analysis, user’s browsing context, process modeling and machine learning to bridge this divide. These techniques include: 1) context-directed browsing that uses link context to find relevant information as users move from page to page; 2) change detection that separates the interface from the implementation of web pages and helps users find relevant information in changing web content; and 3) process modeling that helps users find concepts relevant in web transactions. We describe these three techniques within the context of our Hearsay non-visual web browser