| ---
|
| dataset_info:
|
| features:
|
| - name: latex
|
| dtype: string
|
| - name: image
|
| dtype: image
|
| splits:
|
| - name: train
|
| num_bytes: 28836813681
|
| num_examples: 3212312
|
| download_size: 28836813681
|
| dataset_size: 28836813681
|
| configs:
|
| - config_name: default
|
| data_files:
|
| - split: train
|
| path: "*.parquet"
|
| language:
|
| - en
|
| tags:
|
| - mathematics
|
| - latex
|
| - computer-vision
|
| - OCR
|
| size_categories:
|
| - 1M<n<10M
|
| task_categories:
|
| - image-to-text
|
| pretty_name: Mathematical Expressions Dataset
|
| viewer: true
|
| ---
|
|
|
| # 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
|
|
|
| ```python
|
| 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("ToniDO/TeXtract_padding")
|
|
|
| # 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:
|
|
|
| ```python
|
| 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:
|
|
|
| ```bibtex
|
| @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/ToniDO/TeXtract_padding}
|
| }
|
| ```
|
|
|
