Transaction Models for Web Accessibility: Machine Learning Techniques to Bridge the Divide of Web Accessibility

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. This seriously limits the access of people with impaired vision. Existing assistive technology such as screen readers for non-visual web access typically forces users with visual impairments into an inefficient, sequential mode of information access causing considerable information overload. This problem becomes even more prominent when conducting online transactions that often involve a number of steps spanning several pages. But usually one needs to browse only a small fragment of a page to perform a transactional step. We use this observation to develop an automata-based process model that delivers only the ?relevant? page fragments at each transactional step, thereby reducing information overload on such narrow interaction bandwidths. We develop scalable techniques for the automated construction of such transaction models.