Datasets:
image
imagewidth (px) 4
512
| latex
stringlengths 1
188
| sample_id
stringlengths 16
16
| split_tag
stringclasses 1
value | data_type
stringclasses 1
value |
---|---|---|---|---|
V(\tilde{\beta}) | 47f7bccab32dc3b3 | train | human |
|
GL(V)\times S_{n} | 1fe782f3cfdb576c | train | human |
|
\tilde{f}:X\rightarrow M_{f} | 3e223198b4dd0d3e | train | human |
|
\{g_{1},g_{2},g_{3}\} | df7b0ba66278feaa | train | human |
|
\frac{418^{163}}{(197^{4}\cdot10)} | 1f8c6fc99965fda9 | train | human |
|
g(z)=\frac{1}{f(z)-\mu} | ddbb048e0072b06d | train | human |
|
(\begin{matrix}-1&-1\\ 1&0\end{matrix}) | 5529a28442a3dc78 | train | human |
|
(\frac{6}{194})^{\frac{\frac{397}{5}}{86}} | b1681d8e02998814 | train | human |
|
\Xi_{q}^{z} | 19d93323adcbc41f | train | human |
|
(\begin{matrix}n\\ k\end{matrix}) | 1f44abbf8c209ae9 | train | human |
|
((\frac{5}{17})^{1})^{(270^{2}-8)} | 481966bfb89b0170 | train | human |
|
\int vds | 605e2ab6ec9cbd5d | train | human |
|
u_{0}=\frac{4}{(n\pi)^{2}}dQU_{el} | 05607d04dfabb922 | train | human |
|
w=\sum_{i=1}^{m}\alpha_{i}u_{i}\otimes v_{i} | c4f882c57c31075c | train | human |
|
Z_{y^{\beta}} | 7adb1a9ee3b99bdc | train | human |
|
\beta_{s+2}(q)=\frac{\lambda_{s+2}(q)}{q} | 9692d94c6da19b1e | train | human |
|
t_{1}=\frac{AB}{c+v}+\frac{DE}{\frac{c}{n}-v} | acb4b9a6f178f9fb | train | human |
|
0\notin F | 970d78229d6ae142 | train | human |
|
(\begin{matrix}1&d\\ 0&1\end{matrix}) | 541b298e69c8ee7c | train | human |
|
\nu | e709cbc456ffba96 | train | human |
|
log_{b}(\sqrt[y]{x})=\frac{log_{b}(x)}{y} | 62c4c94aad313640 | train | human |
|
E=\frac{mz^{3}}{\sqrt{1+\frac{f^{3}}{z^{3}}}} | 8ccd0874a8c9e5d0 | train | human |
|
\overline{lim} | e48c7dcd44b0a566 | train | human |
|
\frac{\partial u}{\partial y} | ec78011f9f137af6 | train | human |
|
A\overline{B} | 991c85c32bcb3062 | train | human |
|
z=-1(L_{3}) | 2e870714c27cba01 | train | human |
|
\frac{3}{4}V_{g}+V_{e} | 84f5050333f3c083 | train | human |
|
\int_{L}f(z)dz | 4938cf8feab02a50 | train | human |
|
(\frac{248}{10}/372-43) | 8a314f384ee0045a | train | human |
|
\frac{du}{dy} | 21e0d4756e4cc230 | train | human |
|
\vec{r}=[\begin{matrix}1\\ 1\end{matrix}] | f08f3eb5b06cd29d | train | human |
|
3\frac{dy}{dx}=5y^{\frac{2}{5}} | c1dbecb8e2be50b3 | train | human |
|
|\mu_{3,1}|>\frac{1}{2} | 197e98232402efed | train | human |
|
P=P_{0}exp(-\frac{z}{H}) | 0bdb851944c4943a | train | human |
|
P=K\rho^{1+1/n} | 512cb24c84ab7b40 | train | human |
|
\tilde{C}_{9} | f9fc9e19e7d719a0 | train | human |
|
\hat{c}_{V} | 7fabf7676f78a2ce | train | human |
|
(\begin{matrix}a\\ a+1\end{matrix})=0 | 2b6417c1fe489d8a | train | human |
|
\int ydA=0 | 0a1ca331dbcbbec1 | train | human |
|
\frac{dy}{dx}=0 | 12cb7ac361ddb9df | train | human |
|
\gamma_{2}=2/(1+\sqrt{2}) | 13c7c91616763a6b | train | human |
|
\dot{S}_{i}=-\int_{1}^{2}\frac{\dot{V}}{T}dp | 1b93327deed5d81b | train | human |
|
\alpha=\frac{R_{100}-R_{0}}{100R_{0}} | 162a6ac8303a3f44 | train | human |
|
F=2\pi\int_{-1}^{+1}I\mu d\mu | 4f486c9140057807 | train | human |
|
\sqrt{\frac{5}{126}} | 950566418b8d8e5d | train | human |
|
m=(\frac{t_{2}}{n}) | 7c32a6c77e33e977 | train | human |
|
\frac{1}{4}+\frac{1}{4}=\frac{1}{2} | 54ecd1584329ec81 | train | human |
|
T_{h}\cdot x=0 | d484f15f8f423278 | train | human |
|
\tilde{u}(x) | 94d3a01f4f91f5a9 | train | human |
|
60^{\circ}\cdot(2-\frac{R-B}{G-B}) | b38c26bafc625416 | train | human |
|
\int_{A}(g-f)d\mu=0 | 0353063fe25bc4a9 | train | human |
|
\sum_{i\in\mathbb{N}}X^{i}Y | 8ea1c2a49921f8d4 | train | human |
|
(\begin{matrix}0&0\\ 0&1\end{matrix}) | 110e39a23175ba05 | train | human |
|
\eta=\frac{1}{\mu_{0}\sigma_{0}} | 718f6b7f74f83fb5 | train | human |
|
\sum_{k=2}^{m/2}\frac{m(m-k-1)!}{(m-2k)!k!} | 58340d2303199118 | train | human |
|
b^{n}=\frac{V_{n}-U_{n}\sqrt{D}}{2} | 0d35029560860783 | train | human |
|
P_{3}=(0,-72,2\sqrt{3},12) | e2a88454346d81a6 | train | human |
|
\int_{a}^{b}f(x)g^{\prime}(x)dx | 43dfdddec08f9c86 | train | human |
|
(352+\sqrt{2})^{((2+361)+\sqrt{2})} | c0d7b4c87640a263 | train | human |
|
\frac{{(1-e^{-\alpha})}^{2}}{1+2\alpha e^{-\alpha}-e^{-2\alpha}} | 387fbde52292ffa0 | train | human |
|
lim_{b\rightarrow0}\frac{a}{b} | 86e5a8b6a8b41bb0 | train | human |
|
\hat{4} | 503aa97cdf35beef | train | human |
|
[\begin{matrix}1&R\\ 0&1\end{matrix}] | 7a300985aad006cd | train | human |
|
g_{(x_{1})} | 78acb1cd04a28e6b | train | human |
|
(m,\epsilon) | 53e0da18f282642e | train | human |
|
B([0,\infty)) | ae255ae5ceedb62e | train | human |
|
Q_{ROT}^{\prime}\rightarrow Q_{BEND}^{\prime} | 0dda8cfa6497c976 | train | human |
|
\int dy=\int f^{\prime}(x)dx | ef7cdebb9ba47779 | train | human |
|
\mu=\frac{2N_{+}N_{-}}{N}+1 | ff43f222c6baa2fc | train | human |
|
|0\rangle=[\begin{matrix}1\\ 0\\ \end{matrix}] | b013743dd7c76c86 | train | human |
|
[x,x+O(x^{21/40})] | 0d929374b5c71ff5 | train | human |
|
[\begin{matrix}0\\ 1\end{matrix}] | fb6c677e4d63785e | train | human |
|
lim_{n\rightarrow\infty}T_{n}=T | c7c4577ce79bb347 | train | human |
|
W=P_{p}/(\hbar\omega_{p}) | a53f2aadcd94c4c9 | train | human |
|
(\begin{matrix}9\\ 4\end{matrix}) | 0a863b72dd6da0a3 | train | human |
|
d\tilde{n}/d\eta | 0721790118709f29 | train | human |
|
\lambda | fd34339e7eb797cd | train | human |
|
f(v)=\lambda(v,...,v) | d0be3dad421d5002 | train | human |
|
f(\epsilon_{p}) | 120339ed522753e8 | train | human |
|
(\begin{matrix}p\\ n\end{matrix}) | 624009d81ccc6a3e | train | human |
|
\tau_{w}=\frac{1}{8}fkpMa^{2} | 11a9f7bdd4fb53e1 | train | human |
|
R_{T}=\frac{k\sigma_{T}(1-\nu)}{\alpha E} | 97e560df2251fb7b | train | human |
|
5^{\sqrt{189}}-381\cdot(\frac{197}{10})^{5} | 938ecff9a43989c5 | train | human |
|
V=\frac{dW}{dq},I=\frac{dq}{dt} | b31d1f2cc1f1fadf | train | human |
|
\int fdg | ed8f6118d8fcae38 | train | human |
|
\tilde{E}_{6}(q) | 42955777672b3073 | train | human |
|
\tilde{f}(x,y) | 3da53be2c87e01c3 | train | human |
|
\frac{\partial F_{k_{1}}}{\partial\phi} | 19e26d626304d583 | train | human |
|
\int dV|\Psi|^{2}=N | 27c5336db0cd1241 | train | human |
|
T^{a}=\frac{\lambda^{a}}{2} | 57ca7b2c44234a25 | train | human |
|
\tilde{C}_{5} | 52a109b799fe74bb | train | human |
|
4^{\sqrt{8}}+10-\frac{10}{9}-8 | 93508e2dc460c14d | train | human |
|
q=\frac{m\cdot b}{\sqrt{6-\frac{b^{7}}{c^{7}}}} | e01c36e666f3809f | train | human |
|
J=-D\frac{\partial C}{\partial x} | 48232605e1e66b91 | train | human |
|
\frac{\frac{(39\cdot3)}{6}}{189^{220}} | c625fd1d26600b81 | train | human |
|
\Delta T=\frac{T_{1}}{T_{2}} | 9bf5f8c43bfd9295 | train | human |
|
\tilde{C}_{2} | 94f40453f997846a | train | human |
|
|\begin{matrix}a&b\\ c&d\end{matrix}| | 652f8e136cd8edec | train | human |
|
\frac{\frac{\frac{5}{322}}{10}}{(\frac{\sqrt{10}}{70})^{8}} | f135a4bf275ee897 | train | human |
|
(\overline{x}) | d10417b526e9ef00 | train | human |
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
, andtest
. - 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},
}
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