ToniDO/TeXtract_parquet

Mathematical Expressions Dataset

Dataset Description

This dataset contains images of mathematical expressions along with their corresponding LaTeX code. Images will automatically be displayed as thumbnails in Hugging Face's Data Studio.

Dataset Summary

• Number of files: 17 Parquet files
• Estimated number of samples: 3,212,312
• Format: Parquet optimized for Hugging Face
• Features configured for thumbnails: ✅
• Columns:
  • latex: LaTeX code of the mathematical expression (string)
  • image: Image of the mathematical expression (Image with decode=True)

Supported Tasks

• Image-to-Text: Conversion of mathematical expression images to LaTeX code
• OCR: Optical character recognition for mathematical characters
• Mathematical Expression Recognition: Recognition of mathematical expressions

Languages

The dataset contains mathematical expressions that are universal. The LaTeX code and associated metadata are primarily in English.

Dataset Structure

Data Fields

• latex: String with the LaTeX code that generates the mathematical expression.
• image: PIL image containing the rendered mathematical expression.
  • Type: datasets.Image(decode=True)
  • Format: Images are automatically decoded to PIL.Image.
  • Thumbnails: Automatically generated in Data Studio.

Data Splits

Split	Examples
train	3,212,312

Usage

from datasets import load_dataset

# Load the dataset
# Make sure to replace {repo_id_placeholder} with your actual Hugging Face repository ID
# For example: "your_username/your_dataset_name"
dataset = load_dataset("{repo_id}")

# Access a sample
sample = dataset['train'][0]
image = sample['image']  # Already a PIL.Image thanks to decode=True
latex_code = sample['latex']

print(f"LaTeX: {latex_code}")
image.show()  # Display the image

# Images will also appear as thumbnails in Data Studio

Visualization

Images will automatically be displayed as thumbnails in Hugging Face's Data Studio thanks to the Features configuration:

from datasets import Features, Value, Image

features = Features({
    "latex": Value("string"),
    "image": Image(decode=True)  # This generates the thumbnails
})

Dataset Creation

This dataset was created by converting WebDataset (.tar) files to Parquet format optimized for Hugging Face.

Source Data

• Original format: WebDataset (.tar)
• Conversion: Using a custom Python script.
• Optimization: Parquet format with Snappy compression (default for pyarrow.parquet.write_table if not specified otherwise, actual compression depends on how your Parquet files were created).
• Features: Explicitly configured for automatic thumbnails.

Technical Details

Image Handling

• Storage: Images are stored as bytes within the Parquet file.
• Decoding: Automatic to PIL.Image when loading the dataset using datasets.Image(decode=True).
• Thumbnails: Automatically generated by Hugging Face Data Studio.
• Compatibility: Works with image formats supported by PIL (Pillow) when decoded.

Considerations for Using the Data

Social Impact of Dataset

This dataset can be used to:

• Improve mathematical expression recognition tools.
• Develop OCR systems specialized in mathematics.
• Create accessibility tools for mathematical content.

Licensing Information

• Apache 2.0

Citation Information

If you use this dataset in your research, please cite it as follows:

@misc{ToniDO_TeXtract_parquet_2025},
  author       = {ToniDO},
  title        = {{TeXtract_parquet (Parquet Format)}},
  year         = {2025},
  publisher    = {Hugging Face},
  version      = {1.0.0},
  url          = {https://huggingface.co/datasets/YOUR_HF_USER/YOUR_DATASET_NAME_HERE}
}