Datasets:
File size: 3,157 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 275085333.421
num_examples: 158479
- name: validation
num_bytes: 11585352.835
num_examples: 6765
download_size: 237789121
dataset_size: 286670686.256
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
license: cc-by-4.0
task_categories:
- image-to-text
size_categories:
- 100K<n<1M
---
# LaTeX-OCR Dataset
## Summary
This dataset was created to train [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR), a model for recognizing LaTeX code from images of mathematical formulas. Each sample consists of a synthetically rendered formula image and its corresponding LaTeX formula. The images were generated from scratch using xelatex with multiple fonts, offering more typographic variety than many other datasets that use a single font (typically Computer Modern).
## Data Sources
Formulas were collected from these sources:
1. **Im2LaTeX-100k** ([Deng et al., 2017](https://arxiv.org/abs/1609.04938v2)): The original dataset consists of LaTeX formulas extracted from scientific papers and paired with low-resolution images.
2. **arXiv Papers**: Additional LaTeX expressions were scraped from publicly available PDFs on arXiv.org using automated tools. Only standalone mathematical expressions were retained.
3. **wikipedia**: A small portion of formulas come from technical wikipedia pages
This dataset uses the LaTeX formulas from the sources above but **re-renders all images** from scratch using `xelatex` and a broader set of fonts.
### Fonts Used
Latin Modern Math, GFSNeohellenicMath.otf, Asana Math, XITS Math, Cambria Math
## Structure
Each dataset entry includes:
- A rendered PNG image of a LaTeX formula
- The original LaTeX string
## Usage
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("lukbl/LaTeX-OCR-dataset", split="train")
# Dataset({
# features: ['image', 'text'],
# num_rows: 158479
# })
# Inspect a sample
sample = dataset[0]
print(sample)
# Output example:
# {
# 'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=192x64 at 0x7F3A38642D10>,
# 'text': '\\widetilde \\gamma _ { \\mathrm { h o p f } } \\simeq \\sum _ { n > 0 } \\widetilde { G } _ { n } { \\frac { ( - a ) ^ { n } } { 2 ^ { 2 n - 1 } } }'
# }
```
## Citation
If you use this dataset, please cite:
```
@misc{deng2017imagetomarkupgenerationcoarsetofineattention,
title={Image-to-Markup Generation with Coarse-to-Fine Attention},
author={Yuntian Deng and Anssi Kanervisto and Jeffrey Ling and Alexander M. Rush},
year={2017},
eprint={1609.04938},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/1609.04938},
}
@misc{blecher2025latexocr,
author = {Lukas Blecher},
title = {LaTeX-OCR: Optical Character Recognition for LaTeX Formulas},
howpublished = {\url{https://github.com/lukas-blecher/LaTeX-OCR}},
year = {2025},
note = {GitHub repository}
}
``` |