PDFluent document AI
Collection
Models and data for PDF layout analysis, heading detection and formula recognition from PDFluent. • 3 items • Updated
annotation_attribution stringclasses 1
value | annotation_json stringlengths 496 126k | annotation_licence stringclasses 1
value | asset stringlengths 75 75 | asset_sha256 stringlengths 64 64 | document_id stringclasses 413
values | kind stringclasses 2
values | page_number int64 1 99 | paper_attribution_json stringclasses 413
values | paper_licence stringclasses 3
values | source stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/7d732fac280a83cf1ee5f7312ff0fef377c0e6ef6ed6259cbb0532e99d151471.pdf | 7d732fac280a83cf1ee5f7312ff0fef377c0e6ef6ed6259cbb0532e99d151471 | arxiv:2608.00815 | page | 3 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/060127ad9f72af29c7a229900537a21a0dd70d6d656a68f426cae48e8271db5c.pdf | 060127ad9f72af29c7a229900537a21a0dd70d6d656a68f426cae48e8271db5c | arxiv:2608.00815 | page | 4 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/ebc33934d35dba19cacfb774ab844d50c711c4cab04dfa18211f62f4751e612b.pdf | ebc33934d35dba19cacfb774ab844d50c711c4cab04dfa18211f62f4751e612b | arxiv:2608.00815 | page | 5 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/61efba12fa527fafeafde522517fd9c573a10e25a88554d8f7c9a01cb09c2aae.pdf | 61efba12fa527fafeafde522517fd9c573a10e25a88554d8f7c9a01cb09c2aae | arxiv:2608.00815 | page | 6 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/8e276a29524290cd8852db1c4cdede14720a24babcfcd6b4b1e20a17dc1fecfa.pdf | 8e276a29524290cd8852db1c4cdede14720a24babcfcd6b4b1e20a17dc1fecfa | arxiv:2608.00815 | page | 7 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/08cce6e3744f22baee0d78aa076d8856cd69dc0bd486696998efc999482d51d4.pdf | 08cce6e3744f22baee0d78aa076d8856cd69dc0bd486696998efc999482d51d4 | arxiv:2608.00815 | page | 8 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/6ef579e8fad923314f8939e157e97cdfdabccb7be0c9056fe0c7123c5ac7812e.pdf | 6ef579e8fad923314f8939e157e97cdfdabccb7be0c9056fe0c7123c5ac7812e | arxiv:2608.00815 | page | 9 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/baa9cb438ff8676d11dec3f3e3e92cb647c3f1b30be5fe6276e8239334cb72b2.pdf | baa9cb438ff8676d11dec3f3e3e92cb647c3f1b30be5fe6276e8239334cb72b2 | arxiv:2608.00815 | page | 10 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/1d8ae35e34416af2994a901d5cade2dcebd19205ec06490878658f39f3121234.pdf | 1d8ae35e34416af2994a901d5cade2dcebd19205ec06490878658f39f3121234 | arxiv:2608.00815 | page | 11 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"block":{"bbox":[156.16,523.17,299.69,24.91],"bbox_pdf":[156.16,243.92,455.85,268.83],"fragments":[{"bbox_pdf":[156.16,243.92,455.85,268.83],"words":[4455,4456,4457,4458,4459,4460,4461,4462,4463,4464,4465,4466]}],"html":null,"kind":"paragraph","latex":"\\begin{aligned} \\displaystyle(DEC\\_PPEN20,C20\\_H15P\\_CS3,user... | CC-BY-4.0 | assets/1d8ae35e34416af2994a901d5cade2dcebd19205ec06490878658f39f3121234.pdf | 1d8ae35e34416af2994a901d5cade2dcebd19205ec06490878658f39f3121234 | arxiv:2608.00815 | formula | 11 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/5f5b66c2c3ae97141cdaeaf9d9d8f743ac6e367075f9c1e520af1f9ba493393e.pdf | 5f5b66c2c3ae97141cdaeaf9d9d8f743ac6e367075f9c1e520af1f9ba493393e | arxiv:2608.00815 | page | 12 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/49e5b98f3300ef934cd21dc56e78e9888d76ce56ad9a1eba682b1c46dd73a955.pdf | 49e5b98f3300ef934cd21dc56e78e9888d76ce56ad9a1eba682b1c46dd73a955 | arxiv:2608.00815 | page | 13 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/50798e299e9dc32d5901fa81a2d3c5b8c512c97286195460c89ef0e1fa0d05e9.pdf | 50798e299e9dc32d5901fa81a2d3c5b8c512c97286195460c89ef0e1fa0d05e9 | arxiv:2608.00815 | page | 14 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2},... | CC-BY-4.0 | assets/045c318111c9de5ea04cd815c5afc4267d66a58c24430eb4cea440771711fccc.pdf | 045c318111c9de5ea04cd815c5afc4267d66a58c24430eb4cea440771711fccc | arxiv:2608.00815 | page | 15 | {"arxiv_id":"2608.00815","authors":"Andr\\'as J. Moln\\'ar, Csaba I. Sidl\\'o, Rita R\\'onai, Domonkos R\\'ozsay","title":"Paris as a 15-Minute City: An Explainable AI Perspective","url":"https://arxiv.org/abs/2608.00815v2","version":2} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00824","authors":"Yang Yang","title":"Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations","url":"https://arxiv.org/abs/2608.00824v1"... | CC-BY-4.0 | assets/e077f9be2aa24ee6a80081dbcd0967b0082b7b1dd528e8c1cbe3ab2201c386c7.pdf | e077f9be2aa24ee6a80081dbcd0967b0082b7b1dd528e8c1cbe3ab2201c386c7 | arxiv:2608.00824 | page | 2 | {"arxiv_id":"2608.00824","authors":"Yang Yang","title":"Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations","url":"https://arxiv.org/abs/2608.00824v1","version":1} | CC-BY-4.0 | arxiv-aligned/dev |
Innovation Trigger B.V., PDFluent ground-truth annotations (2026) | {"annotates":["heading","list_item","table","paragraph","furniture"],"attribution":{"arxiv_id":"2608.00824","authors":"Yang Yang","title":"Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations","url":"https://arxiv.org/abs/2608.00824v1"... | CC-BY-4.0 | assets/bb3fd82952762f7ee9079d11bc81ea7add40eea7e6a234159c1849ec039f31ae.pdf | bb3fd82952762f7ee9079d11bc81ea7add40eea7e6a234159c1849ec039f31ae | arxiv:2608.00824 | page | 3 | {"arxiv_id":"2608.00824","authors":"Yang Yang","title":"Structure-Aware Semantic Chunking with Title-Chain Prefixes: A 1600-Query Evaluation and the Measurement Trap in Text-Transform Ablations","url":"https://arxiv.org/abs/2608.00824v1","version":1} | CC-BY-4.0 | arxiv-aligned/dev |
End of preview. Expand in Data Studio
PDFluent PDF Document Layout Ground Truth Dataset
Block-level layout annotations for born-digital PDFs (headings, paragraphs, tables, reading order) with LaTeX formulas and scan pages. For researchers and developers building document parsing, RAG tools, and LLM pipelines. Made by the team behind PDFluent SDK 1.3.0. PDFluent 1.3.0 ranks above every engine on the published opendataloader-bench leaderboard (as of 3 November 2026)*.
*Overall score 0.9168 (quality="auto", keep_furniture=True, Linux) against the engines listed in the benchmark README on 3 November 2026; submitted as pull request #37 (https://github.com/opendataloader-project/opendataloader-bench/pull/37). Other engines have submissions open that are not yet on the leaderboard.
Use it in two lines
from datasets import load_dataset
pages = load_dataset("PDFluent/pdf-document-layout-ground-truth", "default", split="train")
The PDF behind each row is in this repository at the path in the asset field.
More examples
import json
from datasets import load_dataset
repo = "PDFluent/pdf-document-layout-ground-truth"
pages = load_dataset(repo, "default", split="train").filter(lambda r: r["kind"] == "page")
formulas = load_dataset(repo, "default", split="train").filter(lambda r: r["kind"] == "formula")
scans = load_dataset(repo, "third_party_scans", split="train")
sa = load_dataset(repo, "share_alike", split="train")
row = pages[0]
annotation = json.loads(row["annotation_json"])
print(row["document_id"], row["page_number"], list(annotation))
The PDF behind each row is in this repository at the path in asset:
from huggingface_hub import hf_hub_download
pdf_path = hf_hub_download(repo, row["asset"], repo_type="dataset")
What you get
- CC BY 4.0 annotations for born-digital PDFs (free for commercial use with attribution)
- FinTabNet scans under CDLA-Permissive-1.0 terms
- CC BY-SA 4.0 config for share-alike projects
- Evaluate layout analysis, table extraction, and heading detection models
- Lightly fine-tune formula recognition and reading order systems
What is in it
The dataset has three configs. default: born-digital PDF pages with block-level layout annotations (headings, paragraphs, tables) and display-formula LaTeX. third_party_scans: scan pages from FinTabNet, with IBM's ground truth. share_alike: pages from CC BY-SA papers, with annotations under CC BY-SA.
Fields
Each row links to a PDF page asset by SHA-256. annotation_json is a string because page, scan and formula ground truth schemas differ.
| Field | Meaning |
|---|---|
document_id |
Source document identifier (for example arxiv:...) |
source |
Source collection |
kind |
page, formula or scan |
page_number |
Page in the source document |
asset, asset_sha256 |
Path of the PDF file in this repo and its SHA-256 |
annotation_json |
Ground truth as a JSON string (blocks, boxes, reading order, source text, LaTeX) |
annotation_licence, annotation_attribution |
Licence and credit for the annotation |
paper_licence, paper_attribution_json |
Licence and attribution of the underlying paper |
There is one split, train. There is no evaluation split.
Sampling
This is a sample, not the full ground truth. Documents were selected at document level, stratified by source. The sample supports evaluation and light fine-tuning but is too small to train a model to PDFluent's level.
Holdouts and label quality
Documents in PDFluent's benchmark and holdout sets are excluded. The exclusion registry has partial coverage for some sources. Labels are generated automatically and inherit extraction errors.
Licences and attribution
Our annotations in default are CC BY 4.0: credit 'Innovation Trigger B.V., PDFluent ground-truth annotations (2026)' and link to https://pdfluent.com. Paper content keeps its original licence. third_party_scans keeps CDLA-Permissive-1.0. share_alike annotations are CC BY-SA 4.0.
| Config | Annotation licence | Underlying content |
|---|---|---|
default |
CC BY 4.0 | arXiv (CC BY 4.0, CC0 1.0), PubMed Central (CC BY), synthetic pages |
third_party_scans |
CDLA-Permissive-1.0 | FinTabNet, IBM Research |
share_alike |
CC BY-SA 4.0 | arXiv papers under CC BY-SA 4.0 |
Mandatory credit for default: Innovation Trigger B.V., PDFluent ground-truth annotations (2026), https://pdfluent.com. Keep the credit, the licence link and an indication of your changes. Per-document paper credit is in ATTRIBUTION.tsv. The package as a whole is mixed-source and has no blanket licence; check the config you use.
Licence in plain words
Default config uses our CC BY 4.0 annotations (credit line: Innovation Trigger B.V., PDFluent ground-truth annotations (2026)). Third-party scans follow CDLA-Permissive-1.0 terms. Share-alike config requires CC BY-SA 4.0 for derivatives.
Try PDFluent
Load this dataset in two lines of code. Run the PDFluent SDK on the same PDFs and diff its Markdown against the annotations. See pdfluent.com/docs.
Citation
@misc{pdfluent_layout_ground_truth_2026,
author = {{Innovation Trigger B.V.}},
title = {PDFluent PDF document layout ground truth (document-level sample)},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/PDFluent/pdf-document-layout-ground-truth},
note = {PDFluent, https://pdfluent.com}
}
Links
- Website: https://pdfluent.com
- Documentation: https://pdfluent.com/docs
- SDK on PyPI: https://pypi.org/project/pdfluent/
- SDK source and releases: https://github.com/pdfluent/pdfluent-sdk
- Licence: https://pdfluent.com/license
- Organisation: https://huggingface.co/PDFluent
- Formula recognition model: https://huggingface.co/PDFluent/formula-recognition-onnx
- Heading level classifier: https://huggingface.co/PDFluent/pdf-heading-level-classifier
- Layout ground-truth dataset: https://huggingface.co/datasets/PDFluent/pdf-document-layout-ground-truth
- Collection: https://huggingface.co/collections/PDFluent/pdfluent-document-ai-6ae99736adab8c6d90781ab5
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