  A table arranging data in rows and columns is a very effective data
structure, which has been widely used in business and scientific research.
Considering large-scale tabular data in online and offline documents, automatic
table recognition has attracted increasing attention from the document analysis
community. Though human can easily understand the structure of tables, it
remains a challenge for machines to understand that, especially due to a
variety of different table layouts and styles. Existing methods usually model a
table as either the markup sequence or the adjacency matrix between different
table cells, failing to address the importance of the logical location of table
cells, e.g., a cell is located in the first row and the second column of the
table. In this paper, we reformulate the problem of table structure recognition
as the table graph reconstruction, and propose an end-to-end trainable table
graph reconstruction network (TGRNet) for table structure recognition.
Specifically, the proposed method has two main branches, a cell detection
branch and a cell logical location branch, to jointly predict the spatial
location and the logical location of different cells. Experimental results on
three popular table recognition datasets and a new dataset with table graph
annotations (TableGraph-350K) demonstrate the effectiveness of the proposed
TGRNet for table structure recognition. Code and annotations will be made
publicly available.
