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 …

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 …

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 …

Important information that relates to a specific topic in a document is often organized in
tabular format to assist readers with information retrieval and comparison, which may be …

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 …

Tables organize valuable content in a concise and compact representation. This content is
extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they …

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

Table structure recognition aims to extract the logical and physical structure of unstructured
table images into a machine-readable format. The latest end-to-end image-to-text …

We introduce a new table detection and structure recognition approach named
RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of …