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

doi:10.1016/j.patcog.2022.108946

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Table Structure Recognition and Form Parsing by End-to-End Object Detection and Relation Parsing
	Li, Xiao-Hui1,2 ; Yin, Fei1 ; Dai, He-Sen 1,2; Liu, Cheng-Lin1,2
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2022-12-01
卷号	132 页码:14
通讯作者	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
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recogni-tion for modern documents on the POD 2017, cTDaR 2019 and PubTabNet datasets and template-free form parsing for historical documents on the NAF dataset show that the proposed method can handle various table/form structures and achieve superior performance.(c) 2022 Elsevier Ltd.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.
关键词	Table detection Table structure recognition Template -free form parsing Graph neural network End -to -end training
DOI	10.1016/j.patcog.2022.108946
收录类别	SCI
语种	英语
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]
项目资助者	National Key Research and Development Program ; National Natural Science Foundation of China (NSFC)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000860987400006
出版者	ELSEVIER SCI LTD
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50440
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Liu, Cheng-Lin
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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.