Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context
Documents are often used for knowledge sharing and preservation in business and science, within which are tables that capture most of the critical data. Unfortunately, most documents are stored and distributed as PDF or scanned images, which fail to preserve logical table structure. Recent vision-based deep learning approaches have been proposed to address this gap, but most still cannot achieve state-of-the-art results. We present Global Table Extractor (GTE), a vision-guided systematic framework for joint table detection and cell structured recognition, which could be built on top of any object detection model. With GTE-Table, we invent a new penalty based on the natural cell containment constraint of tables to train our table network aided by cell location predictions. GTE-Cell is a new hierarchical cell detection network that leverages table styles. Further, we design a method to automatically label table and cell structure in existing documents to cheaply create a large corpus of training and test data. We use this to enhance PubTabNet with cell labels and create FinTabNet, real-world and complex scientific and financial datasets with detailed table structure annotations to help train and test structure recognition. Our framework surpasses previous state-of-the-art results on the ICDAR 2013 and ICDAR 2019 table competition in both table detection and cell structure recognition. Further experiments demonstrate a greater than 45% improvement in cell structure recognition when compared to a vanilla RetinaNet object detection model in our new out-of-domain FinTabNet.
The T-Recs Table Recognition and Analysis System
This paper presents a new approach to table structure recognition as well as to layout analysis. The discussed recognition process differs significantly from existing approaches as it realizes a bottom-up clustering of given word segments, whereas conventional table structure recognizers all rely on the detection of some separators such as delineation or significant white space to analyze a page from the top-down. The following analysis of the recognized layout elements is based on the construction of a tile structure and detects row- and/or column spanning cells as well as sparse tables with a high degree of confidence. The overall system is completely domain independent, optionally neglects textual contents and can thus be applied to arbitrary mixed-mode documents (with or without tables) of any language and even operates on low quality OCR documents (e.g. facsimiles).
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