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Localizing page elements/objects such as tables, figures, equations, etc. isthe primary step in extracting information from document images. We propose anovel end-to-end trainable deep network, (CDeC-Net) for detecting tablespresent in the documents. The proposed network consists of a multistageextension of Mask R-CNN with a dual backbone having deformable convolution fordetecting tables varying in scale with high detection accuracy at higher IoUthreshold. We empirically evaluate CDeC-Net on all the publicly availablebenchmark datasets - ICDAR-2013, ICDAR-2017, ICDAR-2019,UNLV, Marmot,PubLayNet, and TableBank - with extensive experiments.  Our solution has three important properties: (i) a single trained modelCDeC-Net{\ddag} performs well across all the popular benchmark datasets; (ii)we report excellent performances across multiple, including higher, thresholdsof IoU; (iii) by following the same protocol of the recent papers for each ofthe benchmarks, we consistently demonstrate the superior quantitativeperformance. Our code and models will be publicly released for enabling thereproducibility of the results.

CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images