  This paper tackles the problem of table structure parsing (TSP) from images
in the wild. In contrast to existing studies that mainly focus on parsing
well-aligned tabular images with simple layouts from scanned PDF documents, we
aim to establish a practical table structure parsing system for real-world
scenarios where tabular input images are taken or scanned with severe
deformation, bending or occlusions. For designing such a system, we propose an
approach named Cycle-CenterNet on the top of CenterNet with a novel
cycle-pairing module to simultaneously detect and group tabular cells into
structured tables. In the cycle-pairing module, a new pairing loss function is
proposed for the network training. Alongside with our Cycle-CenterNet, we also
present a large-scale dataset, named Wired Table in the Wild (WTW), which
includes well-annotated structure parsing of multiple style tables in several
scenes like the photo, scanning files, web pages, \emph{etc.}. In experiments,
we demonstrate that our Cycle-CenterNet consistently achieves the best accuracy
of table structure parsing on the new WTW dataset by 24.6\% absolute
improvement evaluated by the TEDS metric. A more comprehensive experimental
analysis also validates the advantages of our proposed methods for the TSP
task.
