# ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents

arXiv: https://arxiv.org/abs/2305.14962 · HTML: https://mlsanity.com/paper/2305.14962/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [Focal Modulation Networks](https://mlsanity.com/paper/2203.11926/) (2022)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [DiT: Self-supervised Pre-training for Document Image Transformer](https://mlsanity.com/paper/2203.02378/) (2022)
- [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://mlsanity.com/paper/2206.01062/) (2022)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations](https://mlsanity.com/paper/2105.06220/) (2021)
- [EvalAI: Towards Better Evaluation Systems for AI Agents](https://mlsanity.com/paper/1902.03570/) (2019)
- [Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale](https://mlsanity.com/paper/1806.02284/) (2018)
- [Robust PDF Document Conversion Using Recurrent Neural Networks](https://mlsanity.com/paper/2102.09395/) (2021)
- [WeLayout: WeChat Layout Analysis System for the ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://mlsanity.com/paper/2305.06553/) (2023)

## Built on by (most cited first, 6 of 6)

- [RoDLA: Benchmarking the Robustness of Document Layout Analysis Models](https://mlsanity.com/paper/2403.14442/) (2024)
- [ESG Accountability Made Easy: DocQA at Your Service](https://mlsanity.com/paper/2311.18481/) (2023)
- [AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization](https://mlsanity.com/paper/2503.22526/) (2025)
- [CHAOS: Chart Analysis with Outlier Samples](https://mlsanity.com/paper/2505.17235/) (2025)
- [The COTe score: A decomposable framework for evaluating Document Layout Analysis models](https://mlsanity.com/paper/2603.12718/) (2026)
- [Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation](https://mlsanity.com/paper/2308.14397/) (2023)

## Abstract (arXiv)

Transforming documents into machine-processable representations is a challenging task due to their complex structures and variability in formats. Recovering the layout structure and content from PDF files or scanned material has remained a key problem for decades. ICDAR has a long tradition in hosting competitions to benchmark the state-of-the-art and encourage the development of novel solutions to document layout understanding. In this report, we present the results of our textitICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents, which posed the challenge to accurately segment the page layout in a broad range of document styles and domains, including corporate reports, technical literature and patents. To raise the bar over previous competitions, we engineered a hard competition dataset and proposed the recent DocLayNet dataset for training. We recorded 45 team registrations and received official submissions from 21 teams. In the presented solutions, we recognize interesting combinations of recent computer vision models, data augmentation strategies and ensemble methods to achieve remarkable accuracy in the task we posed. A clear trend towards adoption of vision-transformer based methods is evident. The results demonstrate substantial progress towards achieving robust and highly generalizing methods for document layout understanding.