A YOLOBased Table Detection Method Due to various table layouts and styles, table detection is always a difficult task in the field of document analysis. Inspired by the great progress of deep learning based methods on object detection, in this paper, we present a YOLObased method for this task. Considering the large difference between document objects and natural objects, we introduce some adaptive adjustments to YOLOv3, including an anchor optimization strategy and two post processing methods. For anchor optimization, we use kmeans clustering to find anchors which are more suitable for tables rather than natural objects and make it easier for our model to find exact positions of tables. In postprocessing process, the extra whitespaces and noisy page objects (e.g. page headers, page footers) are removed from the predicted results, so that our model can get more accurate table margins and higher IoU scores. The proposed method is evaluated on two datasets from ICDAR 2013 Table Competition and ICDAR 2017 Page Object Detection (POD) Competition and achieves stateoftheart performance.

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.