Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs,
and various other document types, a flurry of table pre-training frameworks have been …

Tables are everywhere, from scientific journals, articles, websites, and newspapers all the
way to items we buy at the supermarket. Detecting them is thus of utmost importance to …

We propose TUTA, a unified pre-training architecture for understanding generally structured
tables. Noticing that understanding a table requires spatial, hierarchical, and semantic …

Document structure analysis, such as zone segmentation and table recognition, is a
complex problem in document processing and is an active area of research. The recent …

Table structure recognition is a challenging task due to the various structures and
complicated cell spanning relations. Previous methods handled the problem starting from …

Tabular structures in documents offer a complementary dimension to the raw textual data,
representing logical or quantitative relationships among pieces of information. In digital mail …

Due to various table layouts and styles, table detection is always a difficult task in the field of
document analysis. Inspired by the great progress of deep learning based methods on …

Tabular data is a way to structure, organize, and present information conveniently and
effectively. Real-world tables present data in two dimensions by arranging cells in matrices …

CSV is a popular Open Data format widely used in a variety of domains for its simplicity and
effectiveness in storing and disseminating data. Unfortunately, data published in this format …

Tables are probably the most natural way to represent relational data in various media and
formats. They store a large number of valuable facts that could be utilized for question …