Datasets:

baidu
/

MarathonDocBench

---
license: apache-2.0
task_categories:
  - image-to-text
language:
  - zh
  - en
tags:
  - document-parsing
  - ocr
  - multi-page
  - long-document
  - benchmark
size_categories:
  - n<1K
pretty_name: MarathonDocBench
configs:
  - config_name: default
    data_files:
      - split: test
        path:
          - images/metadata.jsonl
          - images/**/*.png
---
<div align="center">

<img src="assets/baidu.png" width="40%" alt="Baidu Inc." />

</div>

<hr>

<h1 align="center">MarathonDocBench</h1>

<div align="center">
  <a href="https://github.com/baidu/Unlimited-OCR">
    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white" />
  </a>  
  <a href="https://huggingface.co/baidu/Unlimited-OCR2">
    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-ffc107?color=ffc107&logoColor=white" />
  </a>
</div>
<div align="center">
    <a href="https://huggingface.co/baidu/Unlimited-OCR-2/blob/main/Unlimited-OCR-2.pdf">
    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-Unlimited OCR Works 2-b31b1b?logo=arxiv&logoColor=white" />
  </a>
  <a href="https://x.com/Baidu_Inc" target="_blank">
    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Baidu Inc.-white?logo=x&logoColor=white" />
  </a>
</div>


<h3 align="center">A New Benchmark for Ultra-Long Document Parsing: Targeting Hundred-Page-Scale Documents.</h3>

<!-- <div align="center">

![MarathonDocBench leaderboard](assets/leaderboard.png)

</div> -->

MarathonDocBench is the first benchmark specifically designed for **hundred-page document parsing**. It contains 100 documents spanning **100–300 pages** (~18k pages, ~205k annotated layout elements, mixed Chinese/English), with rich cross-page structures — cross-page text and tables appear frequently and densely across documents. Structural annotations (bounding boxes, block type, hierarchy, reading order, and cross-page continuations of text and tables) are initialized from a strong page-by-page parser and carefully refined by human annotators; content transcriptions are verified through an iterative VLM-based refinement pipeline, with flagged disagreements adjudicated by human annotators.


<div align="center">

<img src="assets/overview.png" width="1000" alt="MarathonDocBench overview" />

</div>

This repository ships the **data** (page images + ground-truth annotations).
Evaluation reuses the **MPDocBench-Parse** toolkit — see *Usage* below.

---

## 1. Dataset

```
MarathonDocBench.json        # ground-truth annotations (100 documents)
images/
├── PDF1/page_1.png … 
├── PDF1/page_2.png …  
├── PDF1/ …                  # one folder of page images per document
├── PDF2/page_1.png
└── …                        # ~18k PNG pages total
```


Each entry in `MarathonDocBench.json` follows the MPDocBench schema (`page_info` / `layout_dets` / `extra`): `layout_dets` carries per-element `category_type`, `text`/`html`/`latex`, reading `order`, heading `anno_id`, and `poly` boxes; `extra.relation` lists cross-page `truncated` merges.


---

## 2. Usage

Evaluate with the **MPDocBench-Parse** toolkit; this dataset is a drop-in ground-truth replacement (same schema, same eight metrics).

```bash
# 1. Get the toolkit
git clone https://github.com/Tongyi-Zhiwen/Qwen-Doc.git
cd Qwen-Doc/MPDocBench
pip install -r requirements.txt
pip install mmeval          # needed by the dataset loader, not pinned upstream
# 1b. Replace 3 evaluator files with the accelerated drop-in from THIS repo
#     (metric-identical; only adds doc-level parallelism + a configurable match
#     timeout — vanilla hardcodes a 30s serial match that under-scores ultra-long
#     docs and can take days). Overwrites dataset/end2end_dataset.py,
#     metrics/cal_metric.py and utils/data_preprocess.py:
cp -r /path/to/MarathonDocBench/utils/mpdocbench_patch/* .
# 2. Place this dataset where the config can find it, e.g.
#    data/MarathonDocBench.json   and   data/images/...
# 3. Generate predictions: one Markdown file per document, named <doc_id>.md
#    (e.g. mp3lan_0101.md). Figure tags must follow the required format:
#        ![](pageN_x1_y1_x2_y2.jpg)
#    (N = 1-based page index; x1,y1,x2,y2 = integer coords on a 1000x1000 grid.)
#    Any parser is fine, as long as figure tags follow this format.
# 4. Point configs/end2end.yaml at the dataset (ground_truth.data_path ->
#    data/MarathonDocBench.json, prediction.data_path -> predictions/your_model_md),
#    then run with the settings used for the leaderboard. MPDOC_TEDS_TIMEOUT is
#    required: a few tables have tens of thousands of cells and pure-Python TEDS
#    would run for hours on them; past the cap such a table scores 0.
MPDOC_EVAL_NPROC=32 MPDOC_MATCH_TIMEOUT=1800 MPDOC_DOC_BUDGET=0 \
MPDOC_TEDS_NPROC=32 MPDOC_TEDS_TIMEOUT=300 \
    python pdf_validation.py --config ./configs/end2end.yaml
```

Per-metric results are written to `result/`. The leaderboard `Overall` is the mean of the eight metrics — compute it from the per-metric outputs.

> - **Running a general VLM.** Whole-document inference can fail on provider caps: too many input images per request, request/JSON size limits, or the output-token ceiling (long docs truncate mid-table). Our competitor baselines send all pages in one request, progressively downscale images on oversize/timeout and retry, skip docs over the image cap, and leave failures as empty predictions so the denominator stays 100.
> - **Evaluation notes.** Headings must be real Markdown headings (`#`, `##`, …) — emphasis such as `**bold**` is NOT counted as a heading (it will lower HeadingTEDS). Figure page attribution is part of the score: a figure tagged with the wrong `pageN` is matched against the wrong page and penalized by FigureF1. The documents are very long; with the accelerated evaluator above (`MPDOC_EVAL_NPROC` + a large `MPDOC_MATCH_TIMEOUT`) a full 100-document run takes tens of minutes instead of hours.


---

## 3. Leaderboard

Sorted by `Overall` (the mean of the eight metrics), descending. All entries are end-to-end VLM-based parsers.

| Model | Overall | TextEdit | T-TextEdit | TableTEDS | T-TableTEDS | FormulaCDM | ReadOrderEdit | FigureF1 | HeadingTEDS |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **Unlimited OCR Works 2** | **77.06** | **89.6** | **87.7** | **74.8** | **71.1** | **87.9** | **85.3** | **71.8** | **48.2** |
| Kimi-K3 | 33.7 | 52.5 | 33.6 | 27.8 | 26.3 | 40.5 | 38.9 | 21.4 | 28.9 |
| Gemini-3.8-Flash | 28.6 | 48.1 | 34.2 | 23.4 | 19.6 | 26.4 | 31.3 | 18.6 | 27.3 |
| Qwen-3.8-Flash-Next | 27.8 | 45.6 | 24.7 | 25.4 | 21.0 | 28.3 | 32.1 | 24.1 | 21.5 |
| Gemini-3.1-Pro Preview | 27.0 | 48.4 | 23.6 | 28.5 | 21.7 | 30.4 | 34.6 | 18.9 | 9.7 |
| Qwen-3.8-Max | 22.93 | 39.96 | 19.63 | 15.20 | 13.71 | 18.82 | 26.67 | 33.39 | 16.09 |
| Minimax-M3 | 17.97 | 34.09 | 18.22 | 17.79 | 14.84 | 14.20 | 23.73 | 3.67 | 17.16 |
| DeepSeek-4.1-Flash | 12.34 | 26.00 | 6.67 | 12.74 | 12.59 | 10.45 | 15.70 | 6.95 | 7.62 |
| Doubao-Seed-2.1-Turbo | 8.50 | 18.38 | 8.97 | 5.87 | 5.07 | 6.00 | 10.18 | 8.16 | 5.37 |
| GLM-5.3-Flash | 7.51 | 14.01 | 6.43 | 7.12 | 6.65 | 4.50 | 7.70 | 7.10 | 6.55 |
| Doubao-Seed-2.1-Pro | 5.54 | 10.75 | 4.25 | 3.15 | 4.76 | 2.82 | 7.13 | 7.52 | 3.92 |

---

## License & Copyright

Annotations and tooling references are released under Apache-2.0. The page images are derived from publicly accessible documents and are provided **for academic and research use only**; redistribution of the source documents is not granted by this license. If any content raises copyright concerns, contact the maintainers and it will be addressed promptly.

## Acknowledgement

We build on [MPDocBench-Parse](https://github.com/Tongyi-Zhiwen/Qwen-Doc) for the evaluation toolkit and annotation schema, and thank [OmniDocBench](https://github.com/opendatalab/OmniDocBench) and [CDM](https://github.com/opendatalab/UniMERNet/tree/main/cdm) for the metrics and ideas this benchmark reuses.

## Citation

```bibtex
@misc{shu2026unlimitedocrworks2,
      title={Unlimited OCR Works 2}, 
      author={Baidu ERNIE Team},
      year={2026},
      eprint={xx},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={xx}, 
}
```

	---
	license: apache-2.0
	task_categories:
	- image-to-text
	language:
	- zh
	- en
	tags:
	- document-parsing
	- ocr
	- multi-page
	- long-document
	- benchmark
	size_categories:
	- n<1K
	pretty_name: MarathonDocBench
	configs:
	- config_name: default
	data_files:
	- split: test
	path:
	- images/metadata.jsonl
	- images/*/.png
	---

	<div align="center">

	<img src="assets/baidu.png" width="40%" alt="Baidu Inc." />

	</div>

	<hr>

	<h1 align="center">MarathonDocBench</h1>

	<div align="center">
	<a href="https://github.com/baidu/Unlimited-OCR">
	<img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white" />
	</a>
	<a href="https://huggingface.co/baidu/Unlimited-OCR2">
	<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-ffc107?color=ffc107&logoColor=white" />
	</a>
	</div>

	<div align="center">
	<a href="https://huggingface.co/baidu/Unlimited-OCR-2/blob/main/Unlimited-OCR-2.pdf">
	<img alt="arXiv" src="https://img.shields.io/badge/arXiv-Unlimited OCR Works 2-b31b1b?logo=arxiv&logoColor=white" />
	</a>
	<a href="https://x.com/Baidu_Inc" target="_blank">
	<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Baidu Inc.-white?logo=x&logoColor=white" />
	</a>
	</div>



	<h3 align="center">A New Benchmark for Ultra-Long Document Parsing: Targeting Hundred-Page-Scale Documents.</h3>

	<!-- <div align="center">

	![MarathonDocBench leaderboard](assets/leaderboard.png)

	</div> -->

	MarathonDocBench is the first benchmark specifically designed for hundred-page document parsing. It contains 100 documents spanning 100–300 pages (~18k pages, ~205k annotated layout elements, mixed Chinese/English), with rich cross-page structures — cross-page text and tables appear frequently and densely across documents. Structural annotations (bounding boxes, block type, hierarchy, reading order, and cross-page continuations of text and tables) are initialized from a strong page-by-page parser and carefully refined by human annotators; content transcriptions are verified through an iterative VLM-based refinement pipeline, with flagged disagreements adjudicated by human annotators.


	<div align="center">

	<img src="assets/overview.png" width="1000" alt="MarathonDocBench overview" />

	</div>

	This repository ships the data (page images + ground-truth annotations).
	Evaluation reuses the MPDocBench-Parse toolkit — see Usage below.

	---

	## 1. Dataset

	```
	MarathonDocBench.json # ground-truth annotations (100 documents)
	images/
	├── PDF1/page_1.png …
	├── PDF1/page_2.png …
	├── PDF1/ … # one folder of page images per document
	├── PDF2/page_1.png
	└── … # ~18k PNG pages total
	```


	Each entry in `MarathonDocBench.json` follows the MPDocBench schema (`page_info` / `layout_dets` / `extra`): `layout_dets` carries per-element `category_type`, `text`/`html`/`latex`, reading `order`, heading `anno_id`, and `poly` boxes; `extra.relation` lists cross-page `truncated` merges.


	---

	## 2. Usage

	Evaluate with the MPDocBench-Parse toolkit; this dataset is a drop-in ground-truth replacement (same schema, same eight metrics).

	```bash
	# 1. Get the toolkit
	git clone https://github.com/Tongyi-Zhiwen/Qwen-Doc.git
	cd Qwen-Doc/MPDocBench
	pip install -r requirements.txt
	pip install mmeval # needed by the dataset loader, not pinned upstream

	# 1b. Replace 3 evaluator files with the accelerated drop-in from THIS repo
	# (metric-identical; only adds doc-level parallelism + a configurable match
	# timeout — vanilla hardcodes a 30s serial match that under-scores ultra-long
	# docs and can take days). Overwrites dataset/end2end_dataset.py,
	# metrics/cal_metric.py and utils/data_preprocess.py:
	cp -r /path/to/MarathonDocBench/utils/mpdocbench_patch/* .

	# 2. Place this dataset where the config can find it, e.g.
	# data/MarathonDocBench.json and data/images/...

	# 3. Generate predictions: one Markdown file per document, named <doc_id>.md
	# (e.g. mp3lan_0101.md). Figure tags must follow the required format:
	# ![](pageN_x1_y1_x2_y2.jpg)
	# (N = 1-based page index; x1,y1,x2,y2 = integer coords on a 1000x1000 grid.)
	# Any parser is fine, as long as figure tags follow this format.

	# 4. Point configs/end2end.yaml at the dataset (ground_truth.data_path ->
	# data/MarathonDocBench.json, prediction.data_path -> predictions/your_model_md),
	# then run with the settings used for the leaderboard. MPDOC_TEDS_TIMEOUT is
	# required: a few tables have tens of thousands of cells and pure-Python TEDS
	# would run for hours on them; past the cap such a table scores 0.
	MPDOC_EVAL_NPROC=32 MPDOC_MATCH_TIMEOUT=1800 MPDOC_DOC_BUDGET=0 \
	MPDOC_TEDS_NPROC=32 MPDOC_TEDS_TIMEOUT=300 \
	python pdf_validation.py --config ./configs/end2end.yaml
	```

	Per-metric results are written to `result/`. The leaderboard `Overall` is the mean of the eight metrics — compute it from the per-metric outputs.

	> - Running a general VLM. Whole-document inference can fail on provider caps: too many input images per request, request/JSON size limits, or the output-token ceiling (long docs truncate mid-table). Our competitor baselines send all pages in one request, progressively downscale images on oversize/timeout and retry, skip docs over the image cap, and leave failures as empty predictions so the denominator stays 100.
	> - Evaluation notes. Headings must be real Markdown headings (`#`, `##`, …) — emphasis such as `bold` is NOT counted as a heading (it will lower HeadingTEDS). Figure page attribution is part of the score: a figure tagged with the wrong `pageN` is matched against the wrong page and penalized by FigureF1. The documents are very long; with the accelerated evaluator above (`MPDOC_EVAL_NPROC` + a large `MPDOC_MATCH_TIMEOUT`) a full 100-document run takes tens of minutes instead of hours.


	---

	## 3. Leaderboard

	Sorted by `Overall` (the mean of the eight metrics), descending. All entries are end-to-end VLM-based parsers.

	\| Model \| Overall \| TextEdit \| T-TextEdit \| TableTEDS \| T-TableTEDS \| FormulaCDM \| ReadOrderEdit \| FigureF1 \| HeadingTEDS \|
	\|:---\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| Unlimited OCR Works 2 \| 77.06 \| 89.6 \| 87.7 \| 74.8 \| 71.1 \| 87.9 \| 85.3 \| 71.8 \| 48.2 \|
	\| Kimi-K3 \| 33.7 \| 52.5 \| 33.6 \| 27.8 \| 26.3 \| 40.5 \| 38.9 \| 21.4 \| 28.9 \|
	\| Gemini-3.8-Flash \| 28.6 \| 48.1 \| 34.2 \| 23.4 \| 19.6 \| 26.4 \| 31.3 \| 18.6 \| 27.3 \|
	\| Qwen-3.8-Flash-Next \| 27.8 \| 45.6 \| 24.7 \| 25.4 \| 21.0 \| 28.3 \| 32.1 \| 24.1 \| 21.5 \|
	\| Gemini-3.1-Pro Preview \| 27.0 \| 48.4 \| 23.6 \| 28.5 \| 21.7 \| 30.4 \| 34.6 \| 18.9 \| 9.7 \|
	\| Qwen-3.8-Max \| 22.93 \| 39.96 \| 19.63 \| 15.20 \| 13.71 \| 18.82 \| 26.67 \| 33.39 \| 16.09 \|
	\| Minimax-M3 \| 17.97 \| 34.09 \| 18.22 \| 17.79 \| 14.84 \| 14.20 \| 23.73 \| 3.67 \| 17.16 \|
	\| DeepSeek-4.1-Flash \| 12.34 \| 26.00 \| 6.67 \| 12.74 \| 12.59 \| 10.45 \| 15.70 \| 6.95 \| 7.62 \|
	\| Doubao-Seed-2.1-Turbo \| 8.50 \| 18.38 \| 8.97 \| 5.87 \| 5.07 \| 6.00 \| 10.18 \| 8.16 \| 5.37 \|
	\| GLM-5.3-Flash \| 7.51 \| 14.01 \| 6.43 \| 7.12 \| 6.65 \| 4.50 \| 7.70 \| 7.10 \| 6.55 \|
	\| Doubao-Seed-2.1-Pro \| 5.54 \| 10.75 \| 4.25 \| 3.15 \| 4.76 \| 2.82 \| 7.13 \| 7.52 \| 3.92 \|

	---

	## License & Copyright

	Annotations and tooling references are released under Apache-2.0. The page images are derived from publicly accessible documents and are provided for academic and research use only; redistribution of the source documents is not granted by this license. If any content raises copyright concerns, contact the maintainers and it will be addressed promptly.

	## Acknowledgement

	We build on [MPDocBench-Parse](https://github.com/Tongyi-Zhiwen/Qwen-Doc) for the evaluation toolkit and annotation schema, and thank [OmniDocBench](https://github.com/opendatalab/OmniDocBench) and [CDM](https://github.com/opendatalab/UniMERNet/tree/main/cdm) for the metrics and ideas this benchmark reuses.

	## Citation

	```bibtex
	@misc{shu2026unlimitedocrworks2,
	title={Unlimited OCR Works 2},
	author={Baidu ERNIE Team},
	year={2026},
	eprint={xx},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url={xx},
	}
	```