Table recognition is using the computer to automatically understand the table, to detect the position of the table from the document or picture, and to correctly extract and identify the internal structure and content of the table. After earlier mainstream approaches based on heuristic rules and machine learning, the development of deep learning techniques has brought a new paradigm to this field. This review mainly discusses the table recognition problem from five aspects. The first part introduces data sets, benchmarks, and commonly used evaluation indicators. This section selects representative data sets, benchmarks, and evaluation indicators that are frequently used by researchers. The second part introduces the table recognition model. This survey introduces the development of the table recognition model, especially the table recognition model based on deep learning. It is generally accepted that table recognition is divided into two stages: table detection and table structure recognition. This section introduces the models that follow this paradigm (TD and TSR). The third part is the End-to-End method, this section introduces some scholars' attempts to use an end-to-end approach to solve the table recognition problem once and for all and the part are Data-centric methods, such as data augmentation, aligning benchmarks, and other methods. The fourth part is the data-centric approach, such as data enhancement, alignment benchmark, and so on. The fifth part summarizes and compares the experimental data in the field of form recognition, and analyzes the mainstream and more advantageous methods. Finally, this paper also discusses the possible development direction and trend of form processing in the future, to provide some ideas for researchers in the field of table recognition. (Resource will be released at https://github.com/Wa1den-jy/Topic-on-Table-Recognition .)

翻译：表格识别是利用计算机自动理解表格，从文档或图片中检测表格位置，并正确提取和识别表格内部结构与内容的过程。在早期基于启发式规则和机器学习的主流方法之后，深度学习技术的发展为该领域带来了新的范式。本文主要从五个方面探讨表格识别问题。第一部分介绍数据集、基准测试及常用评估指标，该部分选取了研究者们频繁使用的代表性数据集、基准测试和评估指标。第二部分介绍表格识别模型，本文综述了表格识别模型的发展历程，尤其是基于深度学习的表格识别模型。目前普遍认为表格识别分为两个阶段：表格检测和表格结构识别，该部分将介绍遵循这一范式（TD和TSR）的模型。第三部分是端到端方法，该部分介绍了一些学者尝试使用端到端方法一劳永逸地解决表格识别问题的探索，以及数据为中心的方法，如数据增强、对齐基准等方法。第四部分聚焦数据为中心的方法，包括数据增强、对齐基准等。第五部分总结并比较了表格识别领域的实验数据，分析了当前主流且更具优势的方法。最后，本文还讨论了表格处理未来可能的发展方向与趋势，旨在为表格识别领域的研究者提供一些思路。（资源将发布于 https://github.com/Wa1den-jy/Topic-on-Table-Recognition 。）