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An automatic table recognition method for interpretation of tabular data indocument images majorly involves solving two problems of table detection andtable structure recognition. The prior work involved solving both problemsindependently using two separate approaches. More recent works signify the useof deep learning-based solutions while also attempting to design an end to endsolution. In this paper, we present an improved deep learning-based end to endapproach for solving both problems of table detection and structure recognitionusing a single Convolution Neural Network (CNN) model. We proposeCascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascademask R-CNN HRNet) based model that detects the regions of tables and recognizesthe structural body cells from the detected tables at the same time. Weevaluate our results on ICDAR 2013, ICDAR 2019 and TableBank public datasets.We achieved 3rd rank in ICDAR 2019 post-competition results for table detectionwhile attaining the best accuracy results for the ICDAR 2013 and TableBankdataset. We also attain the highest accuracy results on the ICDAR 2019 tablestructure recognition dataset. Additionally, we demonstrate effective transferlearning and image augmentation techniques that enable CNNs to achieve veryaccurate table detection results. Code and dataset has been made available at:https://github.com/DevashishPrasad/CascadeTabNet

CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents