  Tables are information-rich structured objects in document images. While
significant work has been done in localizing tables as graphic objects in
document images, only limited attempts exist on table structure recognition.
Most existing literature on structure recognition depends on extraction of
meta-features from the PDF document or on the optical character recognition
(OCR) models to extract low-level layout features from the image. However,
these methods fail to generalize well because of the absence of meta-features
or errors made by the OCR when there is a significant variance in table layouts
and text organization. In our work, we focus on tables that have complex
structures, dense content, and varying layouts with no dependency on
meta-features and/or OCR.
  We present an approach for table structure recognition that combines cell
detection and interaction modules to localize the cells and predict their row
and column associations with other detected cells. We incorporate structural
constraints as additional differential components to the loss function for cell
detection. We empirically validate our method on the publicly available
real-world datasets - ICDAR-2013, ICDAR-2019 (cTDaR) archival, UNLV, SciTSR,
SciTSR-COMP, TableBank, and PubTabNet. Our attempt opens up a new direction for
table structure recognition by combining top-down (table cells detection) and
bottom-up (structure recognition) cues in visually understanding the tables.
