  We introduce a new table detection and structure recognition approach named
RobusTabNet to detect the boundaries of tables and reconstruct the cellular
structure of each table from heterogeneous document images. For table
detection, we propose to use CornerNet as a new region proposal network to
generate higher quality table proposals for Faster R-CNN, which has
significantly improved the localization accuracy of Faster R-CNN for table
detection. Consequently, our table detection approach achieves state-of-the-art
performance on three public table detection benchmarks, namely cTDaR TrackA,
PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone
network. Furthermore, we propose a new split-and-merge based table structure
recognition approach, in which a novel spatial CNN based separation line
prediction module is proposed to split each detected table into a grid of
cells, and a Grid CNN based cell merging module is applied to recover the
spanning cells. As the spatial CNN module can effectively propagate contextual
information across the whole table image, our table structure recognizer can
robustly recognize tables with large blank spaces and geometrically distorted
(even curved) tables. Thanks to these two techniques, our table structure
recognition approach achieves state-of-the-art performance on three public
benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we
have further demonstrated the advantages of our approach in recognizing tables
with complex structures, large blank spaces, as well as geometrically distorted
or even curved shapes on a more challenging in-house dataset.
