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

doi:10.1016/j.patcog.2022.108946

	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
Source Publication	PATTERN RECOGNITION
ISSN	0031-3203
	2022-12-01
Volume	132 Pages:14
Corresponding Author	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
Abstract	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recogni-tion for modern documents on the POD 2017, cTDaR 2019 and PubTabNet datasets and template-free form parsing for historical documents on the NAF dataset show that the proposed method can handle various table/form structures and achieve superior performance.(c) 2022 Elsevier Ltd.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.
Keyword	Table detection Table structure recognition Template -free form parsing Graph neural network End -to -end training
DOI	10.1016/j.patcog.2022.108946
Indexed By	SCI
Language	英语
Funding Project	National Key Research and Development Program[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61733007] ; National Natural Science Foundation of China (NSFC)[61721004]
Funding Organization	National Key Research and Development Program ; National Natural Science Foundation of China (NSFC)
WOS Research Area	Computer Science ; Engineering
WOS Subject	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS ID	WOS:000860987400006
Publisher	ELSEVIER SCI LTD
Citation statistics
Document Type	期刊论文
Identifier	http://ir.ia.ac.cn/handle/173211/50440
Collection	模式识别国家重点实验室_模式分析与学习
Corresponding Author	Liu, Cheng-Lin
Affiliation	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
First Author Affilication	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author Affilication	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation 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.

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