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With the widespread use of mobile phones and scanners to photograph andupload documents, the need for extracting the information trapped inunstructured document images such as retail receipts, insurance claim forms andfinancial invoices is becoming more acute. A major hurdle to this objective isthat these images often contain information in the form of tables andextracting data from tabular sub-images presents a unique set of challenges.This includes accurate detection of the tabular region within an image, andsubsequently detecting and extracting information from the rows and columns ofthe detected table. While some progress has been made in table detection,extracting the table contents is still a challenge since this involves morefine grained table structure(rows & columns) recognition. Prior approaches haveattempted to solve the table detection and structure recognition problemsindependently using two separate models. In this paper, we propose TableNet: anovel end-to-end deep learning model for both table detection and structurerecognition. The model exploits the interdependence between the twin tasks oftable detection and table structure recognition to segment out the table andcolumn regions. This is followed by semantic rule-based row extraction from theidentified tabular sub-regions. The proposed model and extraction approach wasevaluated on the publicly available ICDAR 2013 and Marmot Table datasetsobtaining state of the art results. Additionally, we demonstrate that feedingadditional semantic features further improves model performance and that themodel exhibits transfer learning across datasets. Another contribution of thispaper is to provide additional table structure annotations for the Marmot data,which currently only has annotations for table detection.

TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images