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

arXiv: https://arxiv.org/abs/2306.03287 · HTML: https://mlsanity.com/paper/2306.03287/

## 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.

## Builds on (most cited first, 19 of 19)

- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [EAST: An Efficient and Accurate Scene Text Detector](https://mlsanity.com/paper/1704.03155/) (2017)
- [Real-time Scene Text Detection with Differentiable Binarization](https://mlsanity.com/paper/1911.08947/) (2019)
- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [Towards Accurate Scene Text Recognition with Semantic Reasoning Networks](https://mlsanity.com/paper/2003.12294/) (2020)
- [StrucTexT: Structured Text Understanding with Multi-Modal Transformers](https://mlsanity.com/paper/2108.02923/) (2021)
- [PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks](https://mlsanity.com/paper/2004.07464/) (2020)
- [Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes](https://mlsanity.com/paper/1904.06535/) (2019)
- [Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution](https://mlsanity.com/paper/2102.06732/) (2021)
- [TRIE: End-to-End Text Reading and Information Extraction for Document Understanding](https://mlsanity.com/paper/2005.13118/) (2020)
- [Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solution](https://mlsanity.com/paper/2305.07498/) (2023)
- [EATEN: Entity-aware Attention for Single Shot Visual Text Extraction](https://mlsanity.com/paper/1909.09380/) (2019)
- [Multimodal Pre-training Based on Graph Attention Network for Document Understanding](https://mlsanity.com/paper/2203.13530/) (2022)
- [GMN: Generative Multi-modal Network for Practical Document Information Extraction](https://mlsanity.com/paper/2207.04713/) (2022)

## Built on by (most cited first, 20 of 20)

- [OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning](https://mlsanity.com/paper/2501.00321/) (2024)
- [FineVision: Open Data Is All You Need](https://mlsanity.com/paper/2510.17269/) (2025)
- [LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding](https://mlsanity.com/paper/2605.27365/) (2026)
- [Reading Order Matters: Information Extraction from Visually-rich Documents by Token Path Prediction](https://mlsanity.com/paper/2310.11016/) (2023)
- [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025)
- [StrucTexTv3: An Efficient Vision-Language Model for Text-rich Image Perception, Comprehension, and Beyond](https://mlsanity.com/paper/2405.21013/) (2024)
- [LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight](https://mlsanity.com/paper/2511.20648/) (2025)
- [MonkeyOCRv2: A Visual-Text Foundation Model for Document AI](https://mlsanity.com/paper/2607.11562/) (2026)
- [Unveiling the Deficiencies of Pre-trained Text-and-Layout Models in Real-world Visually-rich Document Information Extraction](https://mlsanity.com/paper/2402.02379/) (2024)
- [PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks](https://mlsanity.com/paper/2503.04065/) (2025)
- [Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding](https://mlsanity.com/paper/2505.05446/) (2025)
- [Relation-Rich Visual Document Generator for Visual Information Extraction](https://mlsanity.com/paper/2504.10659/) (2025)
- [Class-Agnostic Region-of-Interest Matching in Document Images](https://mlsanity.com/paper/2506.21055/) (2025)
- [UNER: A Unified Prediction Head for Named Entity Recognition in Visually-rich Documents](https://mlsanity.com/paper/2408.01038/) (2024)
- [KIEval: Evaluation Metric for Document Key Information Extraction](https://mlsanity.com/paper/2503.05488/) (2025)
- [Attend to what I say: Highlighting relevant content on slides](https://mlsanity.com/paper/2601.10244/) (2026)
- [Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models](https://mlsanity.com/paper/2604.00161/) (2026)
- [Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems](https://mlsanity.com/paper/2603.29211/) (2026)
- [Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models](https://mlsanity.com/paper/2607.06012/) (2026)
- [Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents](https://mlsanity.com/paper/2609.14352/) (2026)

## 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.