TableTex

Dataset Summary

TableTex is a dataset for table image-to-LaTeX generation, comprising 19,000 renderable LaTeX table annotations extracted from scientific articles on arXiv. Each annotation corresponds to a table image that can be generated locally using the provided generate_images.py script. The rendered images are not included in the repository, which keeps the distributed dataset compact while providing a reproducible image-generation pipeline.

To support complete and compilable table generation, each annotation is provided as a standalone LaTeX document containing the required package declarations, table caption, and table body. The annotations preserve the structure, content, and visual formatting of the source tables.

The source articles cover publications from 2012 to 2025 across six arXiv subject categories: Computer Science (cs), Mathematics (math), Economics (econ), Electrical Engineering and Systems Science (eess), Quantitative Finance (q-fin), and Statistics (stat). Each sample also includes source-level attribution, license, and modification metadata.

TableTex is introduced in:

• CSPO: Alleviating Reward Ambiguity for Structured Table-to-LaTeX Generation
• Paper: https://arxiv.org/abs/2604.10918
• Code: https://github.com/microsoft/CSPO

For additional details, see the TableTex data card in the CSPO repository.

Dataset Structure

Configuration	File	Split	Samples	Purpose
sft	sft_train.json	train	10,000	Supervised fine-tuning
cspo	cspo_train.jsonl	train	5,000	Reinforcement-learning post-training
evaluation	test.jsonl	test	4,000	Evaluation
Total			19,000

The distributed files are:

tabletex/
|-- README.md
|-- generate_images.py
|-- sft_train.json
|-- cspo_train.jsonl
`-- test.jsonl

After image generation, PNG files are stored in subject-specific directories, and the standalone LaTeX files used for compilation are retained under tex/:

tabletex/
|-- tex/
|   |-- cs/image/
|   |-- econ/image/
|   |-- eess/image/
|   |-- math/image/
|   |-- q-fin/image/
|   `-- stat/image/
|-- cs/image/
|-- econ/image/
|-- eess/image/
|-- math/image/
|-- q-fin/image/
`-- stat/image/

All image paths in the annotations are relative to the dataset root. Examples:

eess/image/2010.13713v2_tex_table3.png
stat/image/2501.02454v2_tex_table8.png

Data Instances

SFT configuration

sft_train.json stores samples in a multimodal conversation format:

{
  "messages": [
    {
      "role": "user",
      "content": "<image>Please generate complete LaTeX code for the table in the image, including the table body and the full preamble."
    },
    {
      "role": "assistant",
      "content": "```latex\n\\documentclass{article}\n...\n```"
    }
  ],
  "images": [
    "eess/image/2010.13713v2_tex_table3.png"
  ],
  "source": {
    "title": "Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion",
    "authors": [
      "Setareh Rahimi Taghanaki",
      "Michael Rainbow",
      "Ali Etemad"
    ],
    "url": "https://arxiv.org/abs/2010.13713v2",
    "attribution": "\"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion\" by Setareh Rahimi Taghanaki, Michael Rainbow, and Ali Etemad, arXiv:2010.13713v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.",
    "license": {
      "name": "CC BY-SA 4.0",
      "url": "https://creativecommons.org/licenses/by-sa/4.0/"
    },
    "modifications": [
      "Extracted the table LaTeX from the article source.",
      "Wrapped it as a standalone LaTeX document.",
      "Rendered and cropped it into a PNG image."
    ]
  }
}

Fields:

• messages: multimodal prompt and target LaTeX response.
• images: list containing the relative output path of the table image.
• source: source and rights metadata for the sample.

CSPO and evaluation configurations

cspo_train.jsonl and test.jsonl store one JSON object per line:

{
  "image_path": "stat/image/2501.02454v2_tex_table8.png",
  "subject": "stat",
  "tex_code": "\\documentclass{article}\n...",
  "source": {
    "title": "Finite-Sample Valid Randomization Tests for Monotone Spillover Effects",
    "authors": [
      "Shunzhuang Huang",
      "Xinran Li",
      "Panos Toulis"
    ],
    "url": "https://arxiv.org/abs/2501.02454v2",
    "attribution": "\"Finite-Sample Valid Randomization Tests for Monotone Spillover Effects\" by Shunzhuang Huang, Xinran Li, and Panos Toulis, arXiv:2501.02454v2, licensed under CC BY-SA 4.0. The table was extracted, wrapped as standalone LaTeX, rendered, and cropped by TableTex.",
    "license": {
      "name": "CC BY-SA 4.0",
      "url": "https://creativecommons.org/licenses/by-sa/4.0/"
    },
    "modifications": [
      "Extracted the table LaTeX from the article source.",
      "Wrapped it as a standalone LaTeX document.",
      "Rendered and cropped it into a PNG image."
    ]
  }
}

Fields:

• image_path: relative output path of the table image.
• subject: arXiv subject category.
• tex_code: standalone LaTeX code corresponding to the image.
• source: source and rights metadata for the sample.

Source metadata

Every sample includes:

• source.title: complete title of the source article.
• source.authors: authors of the source article.
• source.url: version-specific arXiv abstract URL.
• source.attribution: attribution statement for the sample.
• source.license.name: exact source-license name.
• source.license.url: URL of the source license.
• source.modifications: transformations performed by TableTex.

Generating the Images

1. Install Dependencies

TableTex image generation requires:

• Python 3.10 or later
• tqdm
• TeX Live providing pdflatex and pdfcrop
• Poppler providing pdftoppm

Install the Python dependency with:

pip install tqdm

Install TeX Live using the official resources:

• TeX Live: Official Website
• Installation Guide: TeX Live Documentation

We recommend installing the full TeX Live distribution to ensure that all LaTeX packages used by the dataset are available.

On Ubuntu/Debian systems, also install the following system dependencies:

sudo apt-get update
sudo apt-get install -y ghostscript poppler-utils

2. Run the Script

Run the following commands from the dataset root:

python generate_images.py sft_train.json
python generate_images.py cspo_train.jsonl
python generate_images.py test.jsonl

The default behavior is to process every record. To process only the first 20 records:

python generate_images.py test.jsonl --limit 20

To use a different output root:

python generate_images.py test.jsonl --output-root ./rendered

3. Output Files

By default, generated PNG images are written directly to:

<dataset-root>/<subject>/image/<filename>.png

The standalone LaTeX files used for compilation are written to:

<dataset-root>/tex/<subject>/image/<filename>.tex

Dataset Creation

TableTex was constructed from LaTeX sources of scientific articles on arXiv. For each sample, the table LaTeX was extracted from an article, wrapped as a standalone document, and associated with version-specific source and licensing metadata. Images can be generated using the included rendering script.

The source articles cover publications from 2012 to 2025 and were selected from the following Creative Commons license families:

• CC BY
• CC BY-SA
• CC0
• CC BY-NC

The exact license applicable to each sample is recorded in its source.license object.

Intended Use

TableTex is intended for research on table image-to-LaTeX generation, including supervised fine-tuning, reinforcement-learning-based post-training, and evaluation of models that reconstruct tables from images as LaTeX code.

Copyright, Attribution, and Licensing

TableTex contains samples derived from arXiv articles released under CC BY, CC BY-SA, CC0, or CC BY-NC licenses. Copyright remains with the respective authors and/or other rightsholders. The applicable license, attribution, and modification information for each sample is recorded in its source metadata.

Users are responsible for complying with the applicable source license when using, adapting, or redistributing the LaTeX annotations or generated images, including any attribution, share-alike, and non-commercial requirements where applicable. Rendering an image locally does not alter the copyright or license terms of the source material.

Citation

If you use TableTex in your work, please cite:

@article{yang2026cspo,
  title={CSPO: Alleviating Reward Ambiguity for Structured Table-to-LaTeX Generation},
  author={Yang, Yunfan and Lan, Cuiling and Sang, Jitao and Lu, Yan},
  journal={arXiv preprint arXiv:2604.10918},
  year={2026}
}