全局表提取器（GTE）：一个基于视觉上下文的联合表识别和单元结构识别框架
Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context
摘要：
文档通常用于商业和科学中的知识共享和保存，其中包含捕获大多数关键数据的表格。不幸的是，大多数文档都是以PDF或扫描图像的形式存储和分发的，这无法保留逻辑表结构。最近提出了基于视觉的深度学习方法来解决这一差距，但大多数仍然无法实现最先进的结果。我们提出了全局表提取器（GTE），一个视觉引导的联合表检测和细胞结构化识别的系统框架，它可以建立在任何目标检测模型之上。与GTE-Table，我们发明了一个新的惩罚的基础上自然细胞遏制约束表训练我们的表网络的细胞位置预测的帮助。GTE-Cell是一个新的分层细胞检测网络，利用表格样式。此外，我们设计了一种方法来自动标记现有文档中的表和单元格结构，以廉价地创建大型训练和测试数据语料库。我们使用它来增强PubTabNet与单元格标签，并创建FinTabNet，真实世界和复杂的科学和金融数据集，详细的表结构注释，以帮助训练和测试结构识别。我们的框架在表格检测和细胞结构识别方面都超过了ICDAR 2013和ICDAR 2019表格竞赛的最新结果，在全表格提取系统中显着提高了5.8%。进一步的实验表明，与我们新的域外FinTabNet中的香草RetinaNet目标检测模型相比，细胞结构识别的改进超过45%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
