Faster RCNN Based Table Detection Combining Corner Locating Table detection in document images has achieved remarkable improvement. However, there is still a problem of inaccurate table boundary locating. This paper proposes Faster RCNN based table detection combining corner locating method. Firstly, coarse table detection and corner locating are implemented through Faster RCNN network. Secondly, those corners belonging to the same tables are grouped by coordinate matching. At the same time, unreliable corners are filtered. Finally, table boundaries are adjusted and refined by corresponding corner group. The proposed method improves the precision of table boundary locating at pixellevel. Experiment results show that our method effectively improves the precision of table detection. It achieves an Fmeasure of 94.9% on ICDAR2017 POD dataset. Compared with traditional Faster RCNN method, our method increases by 2.8% in Fmeasure and 2.1% at pixellevel localization.

TableNet: Deep Learning Model for Endtoend Table Detection and Tabular Data Extraction from Scanned Document Images With the widespread use of mobile phones and scanners to photograph and upload documents, the need for extracting the information trapped in unstructured document images such as retail receipts, insurance claim forms and financial invoices is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular subimages presents a unique set of challenges. This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table. While some progress has been made in table detection, extracting the table contents is still a challenge since this involves more fine grained table structure(rows u0026 columns) recognition. Prior approaches have attempted to solve the table detection and structure recognition problems independently using two separate models. In this paper, we propose TableNet: a novel endtoend deep learning model for both table detection and structure recognition. The model exploits the interdependence between the twin tasks of table detection and table structure recognition to segment out the table and column regions. This is followed by semantic rulebased row extraction from the identified tabular subregions. The proposed model and extraction approach was evaluated on the publicly available ICDAR 2013 and Marmot Table datasets obtaining state of the art results. Additionally, we demonstrate that feeding additional semantic features further improves model performance and that the model exhibits transfer learning across datasets. Another contribution of this paper is to provide additional table structure annotations for the Marmot data, which currently only has annotations for table detection.