  Table recognition (TR) is one of the research hotspots in pattern
recognition, which aims to extract information from tables in an image. Common
table recognition tasks include table detection (TD), table structure
recognition (TSR) and table content recognition (TCR). TD is to locate tables
in the image, TCR recognizes text content, and TSR recognizes spatial ogical
structure. Currently, the end-to-end TR in real scenarios, accomplishing the
three sub-tasks simultaneously, is yet an unexplored research area. One major
factor that inhibits researchers is the lack of a benchmark dataset. To this
end, we propose a new large-scale dataset named Table Recognition Set
(TabRecSet) with diverse table forms sourcing from multiple scenarios in the
wild, providing complete annotation dedicated to end-to-end TR research. It is
the largest and first bi-lingual dataset for end-to-end TR, with 38.1K tables
in which 20.4K are in English\, and 17.7K are in Chinese. The samples have
diverse forms, such as the border-complete and -incomplete table, regular and
irregular table (rotated, distorted, etc.). The scenarios are multiple in the
wild, varying from scanned to camera-taken images, documents to Excel tables,
educational test papers to financial invoices. The annotations are complete,
consisting of the table body spatial annotation, cell spatial logical
annotation and text content for TD, TSR and TCR, respectively. The spatial
annotation utilizes the polygon instead of the bounding box or quadrilateral
adopted by most datasets. The polygon spatial annotation is more suitable for
irregular tables that are common in wild scenarios. Additionally, we propose a
visualized and interactive annotation tool named TableMe to improve the
efficiency and quality of table annotation.
