  Table recognition is using the computer to automatically understand the
table, to detect the position of the table from the document or picture, and to
correctly extract and identify the internal structure and content of the table.
After earlier mainstream approaches based on heuristic rules and machine
learning, the development of deep learning techniques has brought a new
paradigm to this field. This review mainly discusses the table recognition
problem from five aspects. The first part introduces data sets, benchmarks, and
commonly used evaluation indicators. This section selects representative data
sets, benchmarks, and evaluation indicators that are frequently used by
researchers. The second part introduces the table recognition model. This
survey introduces the development of the table recognition model, especially
the table recognition model based on deep learning. It is generally accepted
that table recognition is divided into two stages: table detection and table
structure recognition. This section introduces the models that follow this
paradigm (TD and TSR). The third part is the End-to-End method, this section
introduces some scholars' attempts to use an end-to-end approach to solve the
table recognition problem once and for all and the part are Data-centric
methods, such as data augmentation, aligning benchmarks, and other methods. The
fourth part is the data-centric approach, such as data enhancement, alignment
benchmark, and so on. The fifth part summarizes and compares the experimental
data in the field of form recognition, and analyzes the mainstream and more
advantageous methods. Finally, this paper also discusses the possible
development direction and trend of form processing in the future, to provide
some ideas for researchers in the field of table recognition. (Resource will be
released at https://github.com/Wa1den-jy/Topic-on-Table-Recognition .)
