Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing

doi:10.1016/j.patcog.2022.108946

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 模式分析与学习团队

	Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing
	Li, Xiao-Hui 1,2; Yin, Fei 2; Dai, He-Sen 1,2; Liu, Cheng-Lin 1,2
刊名	PATTERN RECOGNITION
	2022-12-01
卷号	132 页码:14
关键词	Table detection Table structure recognition Template -free form parsing Graph neural network End -to -end training
ISSN号	0031-3203
DOI	10.1016/j.patcog.2022.108946
通讯作者	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
英文摘要	The recognition of two-dimensional structure of tables and forms from document images is a challenge due to the complexity of document structures and the diversity of layouts. In this paper, we propose a graph neural network (GNN) based unified framework named Table Structure Recognition Network (TSR-Net) to jointly detect and recognize the structures of various tables and forms. First, a multi-task fully convolutional network (FCN) is used to segment primitive regions such as text segments and ruling lines from document images, then a GNN is used to classify and group these primitive regions into page objects such as tables and cells. At last, the relationships between neighboring page objects are analyzed using another GNN based parsing module. The parameters of all the modules in the system can be trained end-to-end to optimize the overall performance. Experiments of table detection and structure recogni-tion for modern documents on the POD 2017, cTDaR 2019 and PubTabNet datasets and template-free form parsing for historical documents on the NAF dataset show that the proposed method can handle various table/form structures and achieve superior performance.(c) 2022 Elsevier Ltd. All rights reserved.
资助项目	National Key Research and Development Program[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61721004]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000860987400006
资助机构	National Key Research and Development Program ; National Natural Science Foundation of China (NSFC)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50439]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Liu, Cheng-Lin
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiao-Hui,Yin, Fei,Dai, He-Sen,et al. Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing[J]. PATTERN RECOGNITION,2022,132:14.
APA	Li, Xiao-Hui,Yin, Fei,Dai, He-Sen,&Liu, Cheng-Lin.(2022).Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing.PATTERN RECOGNITION,132,14.
MLA	Li, Xiao-Hui,et al."Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing".PATTERN RECOGNITION 132(2022):14.