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 ---
license: mit
task_categories:
- object-detection
tags:
- document-layout-analysis
- reading-order-detection
- document-understanding
- coco
- benchmark
size_categories:
- n<1K
---
# Chunkr Reading Order Bench - Open Source Subset
Open-source subset of the **Chunkr Reading Order benchmark dataset**, containing **733 professionally annotated documents** with detailed **reading order annotations** across diverse document layouts.
This dataset benchmarks reading order detection models on complex, real-world documents including **financial reports**, **legal contracts**, **research papers**, **medical records**, and more. Each document includes ground truth reading order sequences essential for accurate document understanding and text extraction.
---
### Benchmark Comparison
We evaluated our model against traditional heuristics, machine learning approaches, and state-of-the-art Vision-Language Models (VLMs):
<div align="center">
<img src="analysis/overall_performance_comparison.png" alt="Model Comparison" width="100%"/>
</div>
| Model | Exact Match | Kendall's τ | Spearman's ρ | Position Acc | Inference (ms) |
|-------|------------|-------------|--------------|--------------|----------------|
| **Chunkr Reading Order** | **88.1%** | **98.2%** | **98.5%** | **96.1%** | **44.6** |
| openai/gpt-5 | 74.8% | 85.0% | 85.3% | 82.5% | 15035.2 |
| google/gemini-2.5-pro | 72.4% | 82.1% | 82.5% | 79.3% | 4678.5 |
| x-ai/grok-4-fast | 68.8% | 91.4% | 92.5% | 83.2% | 5661.1 |
| Pairwise XGBoost* | 67.7% | 92.8% | 94.6% | 84.7% | 16.7 |
| Pairwise LightGBM* | 67.4% | 92.9% | 94.9% | 84.3% | 33.6 |
| anthropic/claude-sonnet-4.5 | 53.8% | 84.5% | 87.1% | 73.1% | 919.4 |
| XY-Cut | 51.7% | 84.4% | 85.5% | 68.5% | 1.7 |
| TBLR (Baseline) | 50.3% | 87.5% | 90.4% | 75.2% | 1.7 |
| qwen/qwen3-vl-235b-a22b-instruct | 49.1% | 79.7% | 80.9% | 66.7% | 4668.5 |
| z-ai/glm-4.5v | 46.8% | 74.5% | 75.4% | 61.0% | 8968.0 |
| LayoutLMv3 Base | 9.4% | 12.0% | 13.0% | 23.9% | 44.2 |
\*Pairwise models trained on Chunkr dataset with optimized features
## Dataset Overview
<div align="center">
<img src="analysis/dataset_overview.png" alt="Dataset Overview" width="100%"/>
</div>
### Document Categories
- Financial (14.3%), Legal (13.1%), Medical (10.8%), Government (10.0%)
- Technical (5.0%), Billing (5.2%), Consulting (4.6%), Research (3.5%)
- Education, Tax, Procurement, Historical, Supply Chain
- Real Estate, Patent, Construction, Newspaper, Magazine, Textbook
### Layout Diversity
- **49.4%** Single-column documents
- **12.8%** Double-column documents
- **34.4%** Three-column documents
- **3.4%** Four+ column documents
---
## 📊 Reading Order Complexity
<div align="center">
<img src="analysis/column_distribution_by_category.png" alt="Column Distribution by Category" width="100%"/>
</div>
The dataset spans a wide range of layout complexities:
| Layout Type | Count | Percentage | Description |
|-------------|------:|:----------:|-------------|
| **Single Column** | 362 | 49.4% | Simple linear reading flow |
| **Double Column** | 94 | 12.8% | Academic papers, newsletters |
| **Multi-Column (3+)** | 277 | 37.8% | Newspapers, magazines, complex reports |
### Element Classes (16 types)
| Class | ID | Count | Description |
|-------|:--:|------:|-------------|
| **Text Block** | 1 | 2,866 | Main body text regions |
| **List Item** | 11 | 1,748 | Bulleted or numbered list elements |
| **Title** | 8 | 1,112 | Section and document titles |
| **Page Number** | 12 | 470 | Page numbering elements |
| **Header** | 7 | 429 | Document headers |
| **Footer** | 2 | 402 | Document footers |
| **Table** | 14 | 392 | Tabular data structures |
| **Picture** | 13 | 377 | Images and photographs |
| **Form Region** | 4 | 351 | Form fields and structures |
| **Graphical Item** | 6 | 250 | Charts, diagrams, illustrations |
| **Caption** | 0 | 231 | Figure and table captions |
| **Footnote** | 3 | 156 | Footnote text |
| **Line Number** | 10 | 145 | Line numbering (code, legal docs) |
| **Formula** | 5 | 57 | Mathematical expressions |
| **Legend** | 9 | 41 | Chart and diagram legends |
| **Unknown** | 15 | 240 | Unclassified elements |
---
## Dataset Structure
```
chunkr-reading-order-bench-oss/
├── images/ # 733 document images (PNG format)
├── _annotations.coco.json # COCO format annotations with reading order
├── metadata.csv # Image metadata (doc_category, dimensions, elements, columns)
└── analysis/ # Statistical analysis and visualizations
├── dataset_overview.png
├── reading_order_visualization.png
├── column_distribution_by_category.png
├── reading_order_sample_*.png
└── ...
```
---
### Reading Order Annotations
The dataset provides **professionally annotated reading order sequences** for each document, capturing the natural flow in which humans would read the content. This is critical for:
- **Accurate text extraction** from complex layouts
- **Document understanding** and information retrieval
- **Maintaining semantic coherence** in multi-column documents
- **Preserving logical flow** across mixed layout elements
<div align="center">
<img src="analysis/reading_order_sample_1.png" alt="Single Column Example" width="45%"/>
<img src="analysis/reading_order_sample_2.png" alt="Multi-Column Example" width="45%"/>
</div>
<div align="center">
<img src="analysis/reading_order_sample_3.png" alt="Complex Layout Example" width="45%"/>
<img src="analysis/reading_order_sample_4.png" alt="Mixed Elements Example" width="45%"/>
</div>
<div align="center">
<img src="analysis/reading_order_sample_5.png" alt="Complex Layout Example" width="45%"/>
<img src="analysis/reading_order_sample_6.png" alt="Mixed Elements Example" width="45%"/>
</div>
<div align="center">
<img src="analysis/reading_order_sample_7.png" alt="Complex Layout Example" width="45%"/>
<img src="analysis/reading_order_sample_8.png" alt="Mixed Elements Example" width="45%"/>
</div>
<div align="center">
<img src="analysis/reading_order_sample_9.png" alt="Complex Layout Example" width="45%"/>
<img src="analysis/reading_order_sample_10.png" alt="Mixed Elements Example" width="45%"/>
</div>
The arrows indicate the ground truth reading order progression, showing how the document should be read to maintain semantic coherence.
---
## Dataset Statistics
<div align="center">
<img src="analysis/dataset_summary.png" alt="Dataset Summary" width="80%"/>
</div>
### Raw Layout Examples
<div align="center">
<img src="analysis/source_raw_layouts.png" alt="Source Raw Layouts" width="100%"/>
</div>
---
## Usage
### Load with Hugging Face `datasets`
```python
from datasets import load_dataset
dataset = load_dataset("ChunkrAI/chunkr-reading-order-bench-oss")
for item in dataset['train']:
print(item['doc_category'], item['num_columns'], item['num_elements'])
```
### With Supervision
```python
import supervision as sv
dataset = sv.DetectionDataset.from_coco(
images_directory_path="images",
annotations_path="_annotations.coco.json"
)
print(f"Dataset size: {len(dataset)}")
print(f"Classes: {dataset.classes}")
# Access reading order from annotations
for image_path, image, annotations in dataset:
# Process annotations with reading order information
pass
```
---
## Data Attribution and References
The following tables summarize **datasets and sources** referenced for sampling, benchmarking, or cross-domain balancing in Chunkr Reading Order Bench OSS.
All sources are **publicly accessible** for research or under permissive licenses (MIT, CC-BY, public domain).
---
### 🧾 Financial & Business Documents
| Dataset / Source | Domain | Access Location |
| -------------------------------------------- | ---------------------------------- | ----------------------------------------------------------------------------------- |
| **DocLayNet (IBM)** | Finance, Legal, Patents, Technical | [Hugging Face – ds4sd/DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet) |
| **IIIT-AR-13K** | Financial Reports, Business | [IIIT Hyderabad / GitHub](https://github.com/iiit-datasets) |
| **Kleister Charity / NDA** | Financial & Legal Documents | [GitHub – applicaai/kleister-nda](https://github.com/applicaai/kleister-nda) |
| **SEC Filings (EDGAR)** | Corporate Finance | [SEC EDGAR Bulk API](https://www.sec.gov/edgar/searchedgar/companysearch.html) |
| **McKinsey, BCG, Deloitte Insights Reports** | Consulting, Business Strategy | BCG Insights / McKinsey / Deloitte Portals |
| **SynFinTabs** | Synthetic Financial Tables | [Hugging Face](https://huggingface.co/datasets) |
| **RVL-CDIP** | Business Records, Legal | [Hugging Face – aharley/rvl_cdip](https://huggingface.co/datasets/aharley/rvl_cdip) |
---
### ⚖️ Legal, Government & Regulatory
| Dataset / Source | Domain | Access Location |
| -------------------------------------- | ------------------------- | --------------------------------------------------------------------- |
| **FUNSD** | Government Forms | [FUNSD Dataset Page](https://guillaumejaume.github.io/FUNSD/) |
| **NIST Tax Forms (1988)** | Tax / Payroll | [NIST Special DB 2](https://www.nist.gov/srd/nist-special-database-2) |
| **CourtListener RECAP Archive** | Legal Filings, Case Law | [Free Law Project](https://www.courtlistener.com/) |
| **Cook County Contracts & Amendments** | Legal / Government | [Cook County Open Data Portal](https://datacatalog.cookcountyil.gov/) |
| **GovDocs1 Million Corpus** | Government / Multi-Domain | [DigitalCorpora Repository](https://digitalcorpora.org/) |
---
### 🏥 Medical & Healthcare
| Dataset / Source | Domain | Access Location |
| ------------------------------------------------------- | ---------------------- | ---------------------------------------------------------------- |
| **Synthetic Medical Forms / OMR Scanned Medical Forms** | Healthcare / Admin | [Hugging Face Datasets](https://huggingface.co/datasets) |
| **Synthea Synthetic Patient Records** | Healthcare (EHR) | [AWS Open Data Registry](https://registry.opendata.aws/synthea/) |
| **Blue Cross Blue Shield Sample EOBs** | Healthcare / Insurance | Public Sample PDFs |
| **Handwritten Rx (Kaggle)** | Prescriptions | [Kaggle Dataset](https://www.kaggle.com/) |
---
### 🏢 Real Estate, Construction & Engineering
| Dataset / Source | Domain | Access Location |
| ------------------------------------------ | --------------------------- | ------------------------------------------------------------- |
| **CubiCasa5K** | Floor Plans, Real Estate | [GitHub – CubiCasa5k](https://github.com/CubiCasa/CubiCasa5k) |
| **CISOL / FloorPlanCAD** | Construction / Supply Chain | [Zenodo DOI 10.5281/zenodo.10829550](https://zenodo.org/) |
| **Machine Learning STRUCTural Floor Plan** | Engineering / CAD | [GitHub Repository](https://github.com/) |
| **Lease Agreements / HUD Forms** | Real Estate / Legal | [HUD.gov Forms](https://www.hud.gov/forms) |
| **Property Records / PhilaDox** | Land Records | [PhilaDox OpenData Portal](https://opendataphilly.org/) |
---
### 📚 Academic, Historical & Educational
| Dataset / Source | Domain | Access Location |
| ----------------------------------------------------- | -------------------------- | ------------------------------------------------------------------------------------------ |
| **DocBank** | Academic (ArXiv Preprints) | [GitHub – doc-analysis/DocBank](https://github.com/doc-analysis/DocBank) |
| **PubLayNet** | Scientific Journals | [IBM DAX Project / Kaggle](https://www.kaggle.com/datasets/ibm-object-detection-publaynet) |
| **U-DIADS-Bib / DIVA-HisDB / HJDataset** | Historical Documents | [ICDAR / UniFr / Harvard HisDoc Projects](https://icdar.org/) |
| **Internet Archive "Magazine Rack" & Textbook Scans** | Magazines, Textbooks | [Internet Archive](https://archive.org/) |
| **Standardized Exam Papers** | Education | [College Board, ACT Archives](https://www.collegeboard.org/) |
---
### 🧮 Technical, Manufacturing & Miscellaneous
| Dataset / Source | Domain | Access Location |
| ------------------------------------------------ | --------------------------- | ------------------------------------------------------- |
| **PRImA Layout Analysis** | Technical Publications | [PRImA Dataset](https://www.primaresearch.org/) |
| **Safety Data Sheets (SDS) Online Repositories** | Manufacturing / Chemical | [SDSManager / Chemical Safety](https://sdsmanager.com/) |
| **Disassembly BOM Dataset** | Manufacturing / Electronics | [Figshare](https://figshare.com/) |
| **Tobacco3482 / Tobacco800** | Business Records | [Kaggle Dataset](https://www.kaggle.com/) |
| **Customs Import Declarations (Synthetic)** | Logistics / Customs | [GitHub Dataset](https://github.com/) |
---
## License
**MIT License**
Free to use for **research and commercial purposes**.
Where applicable, individual third-party datasets retain their **original licenses**, all of which allow redistribution or benchmarking under fair-use or research terms.
---
## Citation
If you use this dataset or benchmark, please cite:
```
@dataset{chunkr_reading_order_bench_oss_2025,
title = {Chunkr Reading Order Bench - Open Source Subset},
author = {ChunkrAI},
year = {2025},
publisher = {Lumina AI Inc.},
license = {MIT},
url = {https://huggingface.co/datasets/ChunkrAI/chunkr-reading-order-bench-oss}
}
```