metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: latex
      dtype: string
    - name: sample_id
      dtype: string
    - name: split_tag
      dtype: string
    - name: data_type
      dtype: string
  splits:
    - name: train
      num_bytes: 1308313988.28
      num_examples: 229864
    - name: test
      num_bytes: 50449700.38
      num_examples: 7644
    - name: val
      num_bytes: 92725986.108
      num_examples: 15674
  download_size: 1247446895
  dataset_size: 1451489674.7680001
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: val
        path: data/val-*
task_categories:
  - image-to-text
tags:
  - math
  - latex
  - handwritten
  - ocr
size_categories:
  - 100K<n<1M

Dataset Card for MathWriting

Dataset Summary

The MathWriting dataset contains online handwritten mathematical expressions collected through a prompted interface and rendered to RGB images. It consists of 230,000 human-written expressions, each paired with its corresponding LaTeX string. The dataset is intended to support research in online and offline handwritten mathematical expression (HME) recognition.

Key features:

Online handwriting converted to rendered RGB images.
Each sample is labeled with a LaTeX expression.
Includes splits: train, val, and test.
All samples in this release are human-written (no synthetic data).
Image preprocessing includes resizing (max dimension ≤ 512 px), stroke width jitter, and subtle color perturbations.

Supported Tasks and Leaderboards

Primary Task:

Handwritten Mathematical Expression Recognition (HMER): Given an image of a handwritten formula, predict its LaTeX representation.

This dataset is also suitable for:

Offline HME recognition (from rendered images).
Sequence modeling and encoder-decoder learning.
Symbol layout analysis and parsing in math.

Dataset Structure

Each example has the following structure:

{
    'image': <PIL.Image.Image in RGB mode>,
    'latex': str,  # the latex string"
    'sample_id': str,  # unique identifier
    'split_tag': str,  # "train", "val", or "test"
    'data_type': str,  # always "human" in this version
}

All samples are rendered from digital ink into JPEG images with randomized stroke width and light RGB variations for augmentation and realism.

Usage

To load the dataset:

from datasets import load_dataset

ds = load_dataset("deepcopy/MathWriting-Human")
sample = ds["train"][0]
image = sample["image"]
latex = sample["latex"]

Licensing Information

The dataset is licensed by Google LLC under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0).

Citation

Please cite the following paper if you use this dataset:

@misc{gervais2025mathwritingdatasethandwrittenmathematical,
      title={MathWriting: A Dataset For Handwritten Mathematical Expression Recognition}, 
      author={Philippe Gervais and Anastasiia Fadeeva and Andrii Maksai},
      eprint={2404.10690},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.10690}, 
}