Datasets:
image
imagewidth (px) 283
5.18k
| doc_category
stringclasses 16
values | width
int64 283
5.18k
| height
int64 405
4.49k
| id
int64 7
7.4k
| num_elements
int64 1
129
|
|---|---|---|---|---|---|
government
| 957
| 1,238
| 7
| 37
|
|
government
| 612
| 792
| 8
| 6
|
|
government
| 612
| 792
| 13
| 7
|
|
government
| 612
| 792
| 15
| 8
|
|
government
| 595
| 842
| 26
| 2
|
|
financial
| 3,840
| 2,160
| 34
| 8
|
|
financial
| 1,190
| 1,684
| 36
| 4
|
|
financial
| 802
| 1,162
| 38
| 4
|
|
technical
| 612
| 792
| 67
| 25
|
|
technical
| 1,191
| 1,684
| 70
| 4
|
|
technical
| 612
| 792
| 72
| 7
|
|
technical
| 612
| 792
| 75
| 12
|
|
financial
| 1,191
| 1,684
| 136
| 1
|
|
education
| 595
| 842
| 149
| 25
|
|
education
| 612
| 792
| 161
| 19
|
|
education
| 612
| 792
| 170
| 25
|
|
education
| 595
| 842
| 174
| 9
|
|
education
| 610
| 794
| 208
| 52
|
|
education
| 1,191
| 1,684
| 214
| 2
|
|
education
| 792
| 612
| 227
| 15
|
|
education
| 534
| 792
| 237
| 9
|
|
education
| 842
| 482
| 251
| 12
|
|
education
| 1,191
| 1,684
| 256
| 8
|
|
education
| 595
| 842
| 261
| 10
|
|
education
| 1,224
| 1,584
| 264
| 29
|
|
education
| 595
| 842
| 269
| 10
|
|
education
| 612
| 792
| 270
| 12
|
|
education
| 595
| 842
| 276
| 4
|
|
education
| 612
| 792
| 285
| 9
|
|
education
| 1,224
| 1,584
| 288
| 10
|
|
education
| 540
| 720
| 292
| 3
|
|
education
| 1,224
| 1,584
| 295
| 15
|
|
education
| 3,840
| 2,160
| 299
| 16
|
|
education
| 595
| 842
| 307
| 4
|
|
education
| 595
| 842
| 311
| 17
|
|
textbook
| 459
| 664
| 321
| 13
|
|
textbook
| 842
| 1,191
| 330
| 16
|
|
textbook
| 1,224
| 1,584
| 331
| 10
|
|
textbook
| 476
| 632
| 336
| 11
|
|
government
| 1,191
| 842
| 367
| 3
|
|
government
| 612
| 792
| 381
| 9
|
|
government
| 612
| 859
| 392
| 4
|
|
government
| 595
| 842
| 397
| 4
|
|
financial
| 1,190
| 1,684
| 434
| 4
|
|
research
| 595
| 842
| 442
| 14
|
|
construction
| 1,224
| 792
| 471
| 17
|
|
financial
| 595
| 842
| 489
| 13
|
|
financial
| 1,584
| 1,224
| 491
| 6
|
|
construction
| 2,448
| 1,584
| 511
| 42
|
|
research
| 595
| 842
| 527
| 10
|
|
research
| 612
| 792
| 539
| 4
|
|
financial
| 1,920
| 1,080
| 556
| 6
|
|
financial
| 1,190
| 1,684
| 562
| 5
|
|
research
| 937
| 1,109
| 573
| 10
|
|
research
| 1,584
| 1,224
| 579
| 4
|
|
research
| 1,224
| 1,584
| 587
| 7
|
|
financial
| 1,224
| 1,584
| 611
| 2
|
|
financial
| 1,191
| 842
| 652
| 23
|
|
financial
| 1,190
| 1,684
| 655
| 15
|
|
financial
| 1,191
| 1,684
| 658
| 18
|
|
research
| 1,191
| 1,684
| 666
| 9
|
|
research
| 612
| 792
| 673
| 12
|
|
research
| 595
| 791
| 679
| 15
|
|
research
| 937
| 1,109
| 680
| 9
|
|
financial
| 612
| 792
| 704
| 6
|
|
financial
| 1,191
| 1,683
| 710
| 8
|
|
financial
| 576
| 720
| 719
| 7
|
|
legal
| 1,191
| 1,684
| 732
| 12
|
|
legal
| 595
| 842
| 741
| 22
|
|
legal
| 595
| 842
| 742
| 3
|
|
legal
| 612
| 792
| 748
| 12
|
|
legal
| 594
| 841
| 749
| 7
|
|
research
| 1,191
| 1,684
| 763
| 11
|
|
research
| 1,684
| 1,191
| 775
| 8
|
|
research
| 595
| 842
| 777
| 4
|
|
patent
| 540
| 666
| 784
| 10
|
|
patent
| 3,384
| 4,490
| 790
| 8
|
|
patent
| 1,191
| 1,684
| 811
| 3
|
|
financial
| 612
| 792
| 832
| 18
|
|
financial
| 1,190
| 1,684
| 835
| 16
|
|
financial
| 1,224
| 1,584
| 839
| 15
|
|
financial
| 842
| 595
| 851
| 3
|
|
financial
| 1,191
| 1,684
| 855
| 6
|
|
patent
| 595
| 842
| 856
| 20
|
|
patent
| 1,191
| 1,684
| 860
| 7
|
|
legal
| 608
| 788
| 879
| 5
|
|
legal
| 595
| 842
| 886
| 6
|
|
magazine
| 1,361
| 794
| 893
| 6
|
|
magazine
| 504
| 684
| 901
| 5
|
|
technical
| 1,191
| 1,684
| 910
| 9
|
|
technical
| 703
| 968
| 944
| 30
|
|
technical
| 595
| 842
| 947
| 7
|
|
technical
| 612
| 792
| 948
| 8
|
|
technical
| 595
| 842
| 950
| 6
|
|
technical
| 595
| 842
| 960
| 30
|
|
technical
| 1,224
| 1,584
| 967
| 33
|
|
technical
| 1,224
| 1,584
| 997
| 7
|
|
technical
| 595
| 842
| 1,006
| 8
|
|
technical
| 596
| 843
| 1,031
| 9
|
|
technical
| 595
| 842
| 1,033
| 17
|
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):
| Model | Exact Match | Kendall's ฯ | Spearman's ฯ | 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
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
๐ Reading Order Complexity
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/
โโโ images/ # 733 document images (PNG format)
โโโ _annotations.coco.json # COCO format annotations with reading order
โโโ metadata.csv # Image metadata (doc_category, dimensions, elements, columns)
โโโ analysis/ # Statistical analysis and visualizations
โโโ dataset_overview.png
โโโ reading_order_visualization.png
โโโ column_distribution_by_category.png
โโโ reading_order_sample_*.png
โโโ ...
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
The arrows indicate the ground truth reading order progression, showing how the document should be read to maintain semantic coherence.
Dataset Statistics
Raw Layout Examples
Usage
Load with Hugging Face datasets
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
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).
๐งพ Financial & Business Documents
| Dataset / Source | Domain | Access Location |
|---|---|---|
| DocLayNet (IBM) | Finance, Legal, Patents, Technical | Hugging Face โ ds4sd/DocLayNet |
| IIIT-AR-13K | Financial Reports, Business | IIIT Hyderabad / GitHub |
| Kleister Charity / NDA | Financial & Legal Documents | GitHub โ applicaai/kleister-nda |
| SEC Filings (EDGAR) | Corporate Finance | SEC EDGAR Bulk API |
| McKinsey, BCG, Deloitte Insights Reports | Consulting, Business Strategy | BCG Insights / McKinsey / Deloitte Portals |
| SynFinTabs | Synthetic Financial Tables | Hugging Face |
| RVL-CDIP | Business Records, Legal | Hugging Face โ aharley/rvl_cdip |
โ๏ธ Legal, Government & Regulatory
| Dataset / Source | Domain | Access Location |
|---|---|---|
| FUNSD | Government Forms | FUNSD Dataset Page |
| NIST Tax Forms (1988) | Tax / Payroll | NIST Special DB 2 |
| CourtListener RECAP Archive | Legal Filings, Case Law | Free Law Project |
| Cook County Contracts & Amendments | Legal / Government | Cook County Open Data Portal |
| GovDocs1 Million Corpus | Government / Multi-Domain | DigitalCorpora Repository |
๐ฅ Medical & Healthcare
| Dataset / Source | Domain | Access Location |
|---|---|---|
| Synthetic Medical Forms / OMR Scanned Medical Forms | Healthcare / Admin | Hugging Face Datasets |
| Synthea Synthetic Patient Records | Healthcare (EHR) | AWS Open Data Registry |
| Blue Cross Blue Shield Sample EOBs | Healthcare / Insurance | Public Sample PDFs |
| Handwritten Rx (Kaggle) | Prescriptions | Kaggle Dataset |
๐ข Real Estate, Construction & Engineering
| Dataset / Source | Domain | Access Location |
|---|---|---|
| CubiCasa5K | Floor Plans, Real Estate | GitHub โ CubiCasa5k |
| CISOL / FloorPlanCAD | Construction / Supply Chain | Zenodo DOI 10.5281/zenodo.10829550 |
| Machine Learning STRUCTural Floor Plan | Engineering / CAD | GitHub Repository |
| Lease Agreements / HUD Forms | Real Estate / Legal | HUD.gov Forms |
| Property Records / PhilaDox | Land Records | PhilaDox OpenData Portal |
๐ Academic, Historical & Educational
| Dataset / Source | Domain | Access Location |
|---|---|---|
| DocBank | Academic (ArXiv Preprints) | GitHub โ doc-analysis/DocBank |
| PubLayNet | Scientific Journals | IBM DAX Project / Kaggle |
| U-DIADS-Bib / DIVA-HisDB / HJDataset | Historical Documents | ICDAR / UniFr / Harvard HisDoc Projects |
| Internet Archive "Magazine Rack" & Textbook Scans | Magazines, Textbooks | Internet Archive |
| Standardized Exam Papers | Education | College Board, ACT Archives |
๐งฎ Technical, Manufacturing & Miscellaneous
| Dataset / Source | Domain | Access Location |
|---|---|---|
| PRImA Layout Analysis | Technical Publications | PRImA Dataset |
| Safety Data Sheets (SDS) Online Repositories | Manufacturing / Chemical | SDSManager / Chemical Safety |
| Disassembly BOM Dataset | Manufacturing / Electronics | Figshare |
| Tobacco3482 / Tobacco800 | Business Records | Kaggle Dataset |
| Customs Import Declarations (Synthetic) | Logistics / Customs | GitHub Dataset |
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}
}
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