mlsanityData to 14 Oct 2026

ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images

arXiv 2306.03287 ↗First posted 5 Jun 2023cs.CVcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 5 Jun 2023.

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19 of 23 references.

References · 19

  1. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 553
  2. 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
  3. 2021OCR-free Document Understanding Transformercited by 407
  4. 2019FUNSD: A Dataset for Form Understanding in Noisy Scanned Documentscited by 349
  5. 2021ICDAR2019 Competition on Scanned Receipt OCR and Information Extractioncited by 256
  6. 2021DocFormer: End-to-End Transformer for Document Understandingcited by 256
  7. 2017EAST: An Efficient and Accurate Scene Text Detectorcited by 248
  8. 2019Real-time Scene Text Detection with Differentiable Binarizationcited by 214
  9. 2021Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognitioncited by 194
  10. 2020Towards Accurate Scene Text Recognition with Semantic Reasoning Networkscited by 131
  11. 2021StrucTexT: Structured Text Understanding with Multi-Modal Transformerscited by 90
  12. 2020PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networkscited by 72
7 more
  1. 2019Look More Than Once: An Accurate Detector for Text of Arbitrary Shapescited by 66
  2. 2021Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solutioncited by 64
  3. 2020TRIE: End-to-End Text Reading and Information Extraction for Document Understandingcited by 61
  4. 2023Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solutioncited by 54
  5. 2019EATEN: Entity-aware Attention for Single Shot Visual Text Extractioncited by 29
  6. 2022Multimodal Pre-training Based on Graph Attention Network for Document Understandingcited by 24
  7. 2022GMN: Generative Multi-modal Network for Practical Document Information Extractioncited by 6

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Citing papers per year: 2023: 1; 2024: 4; 2025: 8; 2026: 7.

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  1. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 173
  2. 2025FineVision: Open Data Is All You Needcited by 53
  3. 2026LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decodingcited by 28
  4. 2023Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Predictioncited by 23
  5. 2025OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Modelscited by 23
  6. 2024StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyondcited by 11
  7. 2025LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sightcited by 8
  8. 2026MonkeyOCRv2: A Visual-Text Foundation Model for Document AIcited by 4
  9. 2024Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extractioncited by 3
  10. 2025PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Trickscited by 3
  11. 2025Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understandingcited by 3
  12. 2025Relation-Rich Visual Document Generator for Visual Information Extractioncited by 2
8 more
  1. 2025Class-Agnostic Region-of-Interest Matching in Document Imagescited by 2
  2. 2024UNER: A Unified Prediction Head for Named Entity Recognition in Visually-rich Documentscited by 1
  3. 2025KIEval: Evaluation Metric for Document Key Information Extractioncited by 1
  4. 2026Attend to what I say: Highlighting relevant content on slidescited by 1
  5. 2026Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Modelscited by 1
  6. 2026Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystemscited by 0
  7. 2026Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Modelscited by 0
  8. 2026Beyond Natural Images: Rethinking AI-Generated Image Detection in Documentscited by 0