Extraction of Relevant Images for Boilerplate Removal in Web Browsers
This addresses the issue of clutter-free webpage viewing for users, particularly in dynamic content, but is incremental as it builds on existing boilerplate removal methods.
The paper tackles the problem of accurately detecting and removing boilerplate images in webpages, which current browser reader modes handle poorly, by proposing a classifier that uses rendering information from a headless browser to extract relevant images.
Boilerplate refers to unwanted and repeated parts of a webpage (such as ads or table of contents) that distracts the user from reading the core content of the webpage, such as a news article. Accurate detection and removal of boilerplate content from a webpage can enable the users to have a clutter free view of the webpage or news article. This can be useful in features like reader mode in web browsers. Current implementations of reader mode in web browsers such as Firefox, Chrome and Edge perform reasonably well for textual content in webpages. However, they are mostly heuristic based and not flexible when the webpage content is dynamic. Also they often do not perform well for removing boilerplate content in the form of images and multimedia in webpages. For detection of boilerplate images, one needs to have knowledge of the actual layout of the images in the webpage, which is only possible when the webpage is rendered. In this paper we discuss some of the issues in relevant image extraction. We also present the design of a testing framework to measure accuracy and a classifier to extract relevant images by leveraging a headless browser solution that gives the rendering information for images.