TY - GEN
T1 - ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
AU - Yu, Wenwen
AU - Zhang, Chengquan
AU - Cao, Haoyu
AU - Hua, Wei
AU - Li, Bohan
AU - Chen, Huang
AU - Liu, Mingyu
AU - Chen, Mingrui
AU - Kuang, Jianfeng
AU - Cheng, Mengjun
AU - Du, Yuning
AU - Feng, Shikun
AU - Hu, Xiaoguang
AU - Lyu, Pengyuan
AU - Yao, Kun
AU - Yu, Yuechen
AU - Liu, Yuliang
AU - Che, Wanxiang
AU - Ding, Errui
AU - Liu, Cheng Lin
AU - Luo, Jiebo
AU - Yan, Shuicheng
AU - Zhang, Min
AU - Karatzas, Dimosthenis
AU - Sun, Xing
AU - Wang, Jingdong
AU - Bai, Xiang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85173582869
U2 - 10.1007/978-3-031-41679-8_32
DO - 10.1007/978-3-031-41679-8_32
M3 - 会议稿件
AN - SCOPUS:85173582869
SN - 9783031416781
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 536
EP - 552
BT - Document Analysis and Recognition – ICDAR 2023 - 17th International Conference, Proceedings
A2 - Fink, Gernot A.
A2 - Jain, Rajiv
A2 - Kise, Koichi
A2 - Zanibbi, Richard
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Document Analysis and Recognition, ICDAR 2023
Y2 - 21 August 2023 through 26 August 2023
ER -