Improving accessibility of transaction-centric web objects

Advances in web technology have considerably widened the Web accessibility divide between sighted and blind users. This divide is especially acute when conducting online transactions, e.g., shopping, paying bills, making travel plans, etc. Such transactions span multiple web pages and require that users find clickable objects (e.g., "add-to-cart" button) which are essential for transaction progress. While this is fast and straightforward for sighted users, locating the clickable objects causes considerable strain for blind individuals using screen-reading technology. Screen readers force users to listen to irrelevant information sequentially and provide no interface for identifying relevant clickable objects. This paper addresses the problem of making clickable objects readily accessible, which can substantially reduce the information overload that is otherwise experienced by blind users. A static knowledge base of keywords constructed from the captions of clickable objects does not provide enough learning capability for identifying clickable objects which do not have any captions (e.g., image buttons without alternative text). In this paper, we present an Information Retrieval based technique that uses the context of transaction-centric objects (e.g., "add-to-cart" and "checkout" buttons) to identify and classify them even when their captions are missing. In addition, the technique utilizes a reinforcement mechanism based on user feedback to accommodate previously unseen captions of objects as well as new categories of objects. We provide user study and experimental evidence of the effectiveness of our algorithm.