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*
*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationDocument Analysis and Recognition – ICDAR 2023 - 17th International Conference, Proceedings
EditorsGernot A. Fink, Rajiv Jain, Koichi Kise, Richard Zanibbi
PublisherSpringer Science and Business Media Deutschland GmbH
Pages536-552
Number of pages17
ISBN (Print)9783031416781
DOIs
StatePublished - 2023
Event17th International Conference on Document Analysis and Recognition, ICDAR 2023 - San José, United States
Duration: 21 Aug 202326 Aug 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14188 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Document Analysis and Recognition, ICDAR 2023
Country/TerritoryUnited States
CitySan José
Period21/08/2326/08/23

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