ICDAR 2023从视觉丰富的文档图像中提取结构化文本竞赛

Wenwen Yu, Chengquan Zhang, H. Cao, W. Hua, Bohan Li, Huang-wei Chen, Ming Liu, Mingrui Chen, Jianfeng Kuang, Mengjun Cheng, Yuning Du, Shikun Feng, Xiaoguang Hu, Pengyuan Lyu, Kun Yao, Yu Yu, Yuliang Liu, Wanxiang Che, Errui Ding, Chengxi Liu, Jiebo Luo, Shuicheng Yan, M. Zhang, Dimosthenis Karatzas, Xingchao Sun, Jingdong Wang, Xiang Bai
{"title":"ICDAR 2023从视觉丰富的文档图像中提取结构化文本竞赛","authors":"Wenwen Yu, Chengquan Zhang, H. Cao, W. Hua, Bohan Li, Huang-wei Chen, Ming Liu, Mingrui Chen, Jianfeng Kuang, Mengjun Cheng, Yuning Du, Shikun Feng, Xiaoguang Hu, Pengyuan Lyu, Kun Yao, Yu Yu, Yuliang Liu, Wanxiang Che, Errui Ding, Chengxi Liu, Jiebo Luo, Shuicheng Yan, M. Zhang, Dimosthenis Karatzas, Xingchao Sun, Jingdong Wang, Xiang Bai","doi":"10.48550/arXiv.2306.03287","DOIUrl":null,"url":null,"abstract":"Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot / Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.","PeriodicalId":294655,"journal":{"name":"IEEE International Conference on Document Analysis and Recognition","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images\",\"authors\":\"Wenwen Yu, Chengquan Zhang, H. Cao, W. Hua, Bohan Li, Huang-wei Chen, Ming Liu, Mingrui Chen, Jianfeng Kuang, Mengjun Cheng, Yuning Du, Shikun Feng, Xiaoguang Hu, Pengyuan Lyu, Kun Yao, Yu Yu, Yuliang Liu, Wanxiang Che, Errui Ding, Chengxi Liu, Jiebo Luo, Shuicheng Yan, M. Zhang, Dimosthenis Karatzas, Xingchao Sun, Jingdong Wang, Xiang Bai\",\"doi\":\"10.48550/arXiv.2306.03287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot / Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.\",\"PeriodicalId\":294655,\"journal\":{\"name\":\"IEEE International Conference on Document Analysis and Recognition\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2306.03287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2306.03287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

结构化文本提取是文档人工智能领域最具价值和挑战性的应用方向之一。然而,过去的基准测试的场景是有限的,相应的评估协议通常集中在结构化文本提取方案的子模块上。为了消除这些问题,我们组织了ICDAR 2023关于从视觉丰富文档图像(SVRD)中提取结构化文本的竞赛。我们为SVRD设置了两个轨道,包括轨道1:HUST-CELL和轨道2:百度- fest,其中HUST-CELL旨在评估复杂实体链接和标记的端到端性能,百度- fest侧重于从端到端角度评估零采样/少采样结构化文本提取的性能和泛化。与目前的文档基准测试相比,我们的两条竞赛基准测试极大地丰富了场景,包含了50多种视觉丰富的文档图像(主要来自实际的企业应用)。比赛于2022年12月30日开始,于2023年3月24日结束。轨道1有35个参与者和91个有效提交,轨道2有15个参与者和26个有效提交。在本报告中,我们将介绍动机、竞争数据集、任务定义、评估方案和提交摘要。根据提交作品的表现,我们认为在复杂和零拍摄场景的预期信息提取性能上还有很大的差距。希望这次比赛能够吸引CV和NLP领域的众多研究人员,为文档AI领域带来一些新的思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot / Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信