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V(\tilde{\beta})
47f7bccab32dc3b3
train
human
GL(V)\times S_{n}
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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)}
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train
human
g(z)=\frac{1}{f(z)-\mu}
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train
human
(\begin{matrix}-1&-1\\ 1&0\end{matrix})
5529a28442a3dc78
train
human
(\frac{6}{194})^{\frac{\frac{397}{5}}{86}}
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train
human
\Xi_{q}^{z}
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train
human
(\begin{matrix}n\\ k\end{matrix})
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train
human
((\frac{5}{17})^{1})^{(270^{2}-8)}
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train
human
\int vds
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train
human
u_{0}=\frac{4}{(n\pi)^{2}}dQU_{el}
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train
human
w=\sum_{i=1}^{m}\alpha_{i}u_{i}\otimes v_{i}
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train
human
Z_{y^{\beta}}
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train
human
\beta_{s+2}(q)=\frac{\lambda_{s+2}(q)}{q}
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train
human
t_{1}=\frac{AB}{c+v}+\frac{DE}{\frac{c}{n}-v}
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train
human
0\notin F
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train
human
(\begin{matrix}1&d\\ 0&1\end{matrix})
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train
human
\nu
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train
human
log_{b}(\sqrt[y]{x})=\frac{log_{b}(x)}{y}
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train
human
E=\frac{mz^{3}}{\sqrt{1+\frac{f^{3}}{z^{3}}}}
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train
human
\overline{lim}
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train
human
\frac{\partial u}{\partial y}
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train
human
A\overline{B}
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train
human
z=-1(L_{3})
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train
human
\frac{3}{4}V_{g}+V_{e}
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train
human
\int_{L}f(z)dz
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train
human
(\frac{248}{10}/372-43)
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train
human
\frac{du}{dy}
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train
human
\vec{r}=[\begin{matrix}1\\ 1\end{matrix}]
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train
human
3\frac{dy}{dx}=5y^{\frac{2}{5}}
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train
human
|\mu_{3,1}|>\frac{1}{2}
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train
human
P=P_{0}exp(-\frac{z}{H})
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train
human
P=K\rho^{1+1/n}
512cb24c84ab7b40
train
human
\tilde{C}_{9}
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train
human
\hat{c}_{V}
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train
human
(\begin{matrix}a\\ a+1\end{matrix})=0
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train
human
\int ydA=0
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train
human
\frac{dy}{dx}=0
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train
human
\gamma_{2}=2/(1+\sqrt{2})
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train
human
\dot{S}_{i}=-\int_{1}^{2}\frac{\dot{V}}{T}dp
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train
human
\alpha=\frac{R_{100}-R_{0}}{100R_{0}}
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train
human
F=2\pi\int_{-1}^{+1}I\mu d\mu
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train
human
\sqrt{\frac{5}{126}}
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train
human
m=(\frac{t_{2}}{n})
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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
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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
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train
human
f(v)=\lambda(v,...,v)
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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
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train
human
\tilde{E}_{6}(q)
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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}
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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
End of preview. Expand in Data Studio

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}, 
}
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