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.

ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD) organized with two tracks for evaluating end-to-end Complex Entity Linking and Labeling and Zero-shot / Few-shot Structured Text extraction respectively, with more than 50 types of visually-rich document images from enterprise applications, attracting 35 and 15 participants respectively and revealing a large gap in expected information extraction performance for complex and zero-shot scenarios.