Datasets:
File size: 3,268 Bytes
54e447a 7b64c36 0274c7e 54e447a 7b64c36 0274c7e d0a406b 54e447a ac786c9 54e447a ac786c9 5142b9d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
---
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:
```python
{
'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:
```python
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](https://creativecommons.org/licenses/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},
}
``` |