Towards an Improved Vision-Based Web Page Segmentation Algorithm.

In this paper we introduce an edge-based segmentation algorithm designed for web pages. We consider each web page as an image and perform segmentation as the initial stage of a planned parsing system that will also include region classification. The motivation for our work is to enable improved online experiences for users with assistive needs (serving as the back-end process for such front-end tasks as zooming and decluttering the image being presented to those with visual or cognitive challenges, or producing less unwieldy output from screenreaders). Our focus is therefore on the interpretation of a class of man-made images (where web pages consist of one particular set of these images which have important constraints that assist in performing the processing). After clarifying some comparisons with an earlier model of ours, we show validation for our method. Following this, we briefly discuss the contribution for the field of computer vision, offering a contrast with current work in segmentation focused on the processing of natural images.