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 with a significant 5.8% improvement in the full table
extraction system. 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.

本文介绍了全局表格提取器（GTE），它是一种由视觉引导的系统框架，旨在通过基于自然单元格包含约束的新型罚函数来训练表格网络，以检测表格和单元格结构，结合表格风格，设计一种新的分层单元格检测网络，并定期标记表格和单元格结构以便成为训练数据。实验证明，GTE 在表格检测和单元格结构识别方面超过之前的最先进结果，并且在新的跨域 FinTabNet 数据集上的实验表明，在单元格结构识别方面比 vanilla RetinaNet 的物体检测模型提高了 45% 以上。