ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich
  Document Images
Wenwen Yu Chengquan Zhang Haoyu Cao Wei Hua Bohan Li Huang Chen Mingyu Liu Mingrui Chen Jianfeng Kuang Mengjun Cheng Yuning Du Shikun Feng Xiaoguang Hu Pengyuan Lyu Kun Yao Yuechen Yu Yuliang Liu Wanxiang Che Errui Ding Cheng-Lin Liu Jiebo Luo Shuicheng Yan Min Zhang Dimosthenis Karatzas Xing Sun Jingdong Wang Xiang Bai
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.