Datasets:

rpopek
/

MIDAL

dataset_info:
  features:
    - name: file_name
      dtype: image
    - name: url
      dtype: string
    - name: book
      dtype: string
    - name: description
      dtype: string
    - name: context
      dtype: string
    - name: captions
      dtype: string
  splits:
    - name: full
      num_bytes: 40433558
      num_examples: 2020
    - name: train
      num_bytes: 69779282.08386138
      num_examples: 1990
    - name: test
      num_bytes: 1036459.2574257426
      num_examples: 20
    - name: validation
      num_bytes: 613312.1287128713
      num_examples: 10
  download_size: 137177467
  dataset_size: 111862611.47
configs:
  - config_name: default
    data_files:
      - split: full
        path: data/full-*
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
language:
  - en
tags:
  - math
  - mathematics
  - education
  - accessibility
  - STEM
pretty_name: MIDAL Dataset
size_categories:
  - 1K<n<10K
license: cc-by-nc-sa-4.0

Dataset Card for MIDAL Dataset

The Math Image Descriptions for Accessible Learning (MIDAL) Dataset is a curated collection of 2,020 mathematical figures paired with descriptions, captions, contextual information, and source metadata. It was created to support research in OCR-free math understanding, math image description generation, and improving accessibility in mathematics education.

Citation Requirement: If you use this dataset (or any of its splits) in your research, presentations, or products, please formally cite our associated arXiv paper. See the Citation section below for BibTeX and APA formats.

In this repository, you will find:

the full dataset
the training, testing, and validation subsets used for (paper in progress)

For in-depth information regarding the dataset, please read our paper.

Dataset Details

Dataset Description

The following table depicts the split in the dataset to form the training, testing, and validation subsets used for the paper (link):

Split	Size	Description
Full	2020	The entire dataset (no splits)
Train	1990	Used for model training
Validation	10	Used for hyperparameter tuning
Test	20	Held-out evaluation set

Split Logic

The splits were created by

shuffle dataset with seed = 36
select 30 images for temporary split (seed = 36)
split the temporary split to produce 10 entries for validation and 20 for testing (seed = 36)
the remaining data forms the training subset.

Dataset Metadata

Curated by: Rebeka Popek, Vaghawan Ojha, Young Hwan You
Language(s) (NLP): English
License: CC BY-NC-SA 4.0
Paper: MIDAL: Math Image Descriptions for Accessible Learning

Uses

MIDAL is intended for training and evaluating models in:

OCR-free math description generation and
accessibility for mathematical content.

Out-of-Scope Use

This dataset is not intended to be used commercially as that will violate the Creative Commons copyrights of many sources.

Dataset Features

Field	Type	Description
file_name	Image	The image itself
description	string	Figure description
context	string	Additional contextual information
captions	string	Figure captions that appeared below the image
book	string	Source book title
url	string	Source URL

Dataset Creation

Source Data

The data was collected only from open educational resources (OER). If using this dataset, please respect their intellectual property and copyrights.

Data Collection and Processing

The dataset was produced using

Python's Playwright library to collect the metadata
Label Studio for annotation and
manual curation to ensure quality and accuracy.

Quality checks included:

rewriting image descriptions to fit NWEA's image description guidelines
converting all math content into LaTeX (in amsmath formatting) and
adding white backgrounds to images with transparent backgrounds.

Maintainer

Huggingface: @rpopek

email contact: rebekapopek@gmail.com

Citation

BibTeX:

@misc{popek2026midalmathimagedescriptions,
      title={MIDAL: A Dataset of Math Image Descriptions for Accessible Learning}, 
      author={Rebeka Popek and Vaghawan Ojha and Young Hwan You},
      year={2026},
      eprint={2608.00868},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.00868}, 
}