Improving Web Accessibility: Computing New Web Page Design with NSGA-II for People with Low Vision

As society becomes increasingly aware of the need to take disabilities into account, new information technologies and intensive use of computers can be a chance or create new barriers. In the specific case of people with low vision, efforts to improve e-accessibility are mainly focused on the provision of third-party tools. Assistive technologies like screen magnifiers adapt graphical user interfaces to increase the quality of the perceived information. However, when these technologies deal with the Web, they are not able to meet all specific needs of people with low vision. In this paper, we propose an approach to make Web pages more accessible for users with specific needs. User preferences can concern font size, font family, text color, word and letter spacing, link color and decoration or even more complex features regarding brightness, relative size or contrast. We also take into account and encode the designer's graphical choices as designer preferences. Solving preferences of the user and of the designer to obtain a new Web page design is an optimization problem that we deal with Non-dominated Sorting Genetic Algorithm II (NSGA-II), a polynomial Multi-Objective Genetic Algorithm. We conducted detailed tests and evaluated the running time and quality of results of our tool on real Web pages. The results show that our approach for adapting Web page designs to specific user needs with NSGA II is worthwhile on real Web pages.