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Tables are information-rich structured objects in document images. Whilesignificant work has been done in localizing tables as graphic objects indocument images, only limited attempts exist on table structure recognition.Most existing literature on structure recognition depends on extraction ofmeta-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-featuresor errors made by the OCR when there is a significant variance in table layoutsand text organization. In our work, we focus on tables that have complexstructures, dense content, and varying layouts with no dependency onmeta-features and/or OCR.  We present an approach for table structure recognition that combines celldetection and interaction modules to localize the cells and predict their rowand column associations with other detected cells. We incorporate structuralconstraints as additional differential components to the loss function for celldetection. We empirically validate our method on the publicly availablereal-world datasets - ICDAR-2013, ICDAR-2019 (cTDaR) archival, UNLV, SciTSR,SciTSR-COMP, TableBank, and PubTabNet. Our attempt opens up a new direction fortable structure recognition by combining top-down (table cells detection) andbottom-up (structure recognition) cues in visually understanding the tables.

Table Structure Recognition using Top-Down and Bottom-Up Cues