An automated approach for retrieving hierarchical data from HTML tables
Proceedings of the eighth international conference on Information and knowledge management
A machine learning based approach for table detection on the web
Proceedings of the 11th international conference on World Wide Web
HTML Page Analysis Based on Visual Cues
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Mining tables from large scale HTML texts
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Efficient web browsing on small screens
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Application of content adaptation in web accessibility for the blind
Proceedings of the International Cross-Disciplinary Conference on Web Accessibility
A machine learning based approach for separating head from body in web-tables
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Adapting Web Page Tables on Mobile Devices
International Journal of Handheld Computing Research
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Web table understanding is challenging for people with visual disability. They depend on screen readers to convey the table information. Screen readers present content linearly to users, but if the table is large, the user may have long forgotten the heading before the last row is read. Even in table navigation mode, it can still be confusing if the table is not marked up properly. Though there are guidelines for web developers to create accessible web tables, some authors may still not properly mark up the web tables. There are also lots of legacy web tables that are not designed with accessibility in mind. These unstructured web tables arouse a need for web accessibility improvements. Existing solutions mainly focus on interpreting tables by screen readers and providing guidelines to create accessible web table, so there is a research gap on how to adapt unstructured table to improve web accessibility. In this regard, we propose a method to extract the structure from these tables and re-organize them into multiple levels of abstractions so that the visually impaired users can access the tables level by level by selecting the corresponding option number. This has enhanced the table content understanding for people without visual perception and has greatly improved web accessibility of unstructured web table for both PC users and mobile users with visual disabilities.