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Documents are often used for knowledge sharing and preservation in businessand science, within which are tables that capture most of the critical data.Unfortunately, most documents are stored and distributed as PDF or scannedimages, which fail to preserve logical table structure. Recent vision-baseddeep learning approaches have been proposed to address this gap, but most stillcannot achieve state-of-the-art results. We present Global Table Extractor(GTE), a vision-guided systematic framework for joint table detection and cellstructured recognition, which could be built on top of any object detectionmodel. With GTE-Table, we invent a new penalty based on the natural cellcontainment constraint of tables to train our table network aided by celllocation predictions. GTE-Cell is a new hierarchical cell detection networkthat leverages table styles. Further, we design a method to automatically labeltable and cell structure in existing documents to cheaply create a large corpusof training and test data. We use this to enhance PubTabNet with cell labelsand create FinTabNet, real-world and complex scientific and financial datasetswith detailed table structure annotations to help train and test structurerecognition. Our framework surpasses previous state-of-the-art results on theICDAR 2013 and ICDAR 2019 table competition in both table detection and cellstructure recognition with a significant 5.8% improvement in the full tableextraction system. Further experiments demonstrate a greater than 45%improvement in cell structure recognition when compared to a vanilla RetinaNetobject detection model in our new out-of-domain FinTabNet.

Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context