A Machine Learning Based Approach for Table Detection on The Web*

Yalin Wang, and Jianying Hu


Table is a commonly used presentation scheme, especially for describing relational information. However, table understanding remains an open problem in both document image analysis and information retrieval fields. In this paper, we consider the problem of table detection in web documents. Its potential applications include web mining, knowledge management, and web content summarization and delivery to narrow-bandwidth devices. We describe a machine learning based approach to classify each given table entity as either genuine or non-genuine. Various features reflecting the layout as well as content characteristics of tables are studied. In order to facilitate the training and evaluation of our table classifier, we designed a novel web document table ground truthing protocol and used it to build a large table ground truth database. The database consists of 1,393 HTML files collected from hundreds of different web sites and contains 11,477 leaf elements, out of which 1,740 are genuine tables. Experiments were conducted using the cross validation method and an F-measure of 95.88% was achieved.

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*The web table ground truth database can be downloaded here.