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 …

The first phase of table recognition is to detect the tabular area in a document.
Subsequently, the tabular structures are recognized in the second phase in order to extract …

Image Transformer has recently achieved significant progress for natural image
understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) …

An automatic table recognition method for interpretation of tabular data in document images
majorly involves solving two problems of table detection and table structure recognition. The …

Recent advances in document image analysis (DIA) have been primarily driven by the
application of neural networks. Ideally, research outcomes could be easily deployed in …

Document layout analysis usually relies on computer vision models to understand
documents while ignoring textual information that is vital to capture. Meanwhile, high quality …

Multimodal pre-training with text, layout, and image has achieved SOTA performance for
visually-rich document understanding tasks recently, which demonstrates the great potential …

Recently, significant progress has been made applying machine learning to the problem of
table structure inference and extraction from unstructured documents. However, one of the …

Tables are information-rich structured objects in document images. While significant work
has been done in localizing tables as graphic objects in document images, only limited …

Documents are often the format of choice for knowledge sharing and preservation in
business and science, within which are tables that capture most of the critical data …