---
license: apache-2.0
language:
- en
- zh
pretty_name: UniMER_Dataset
tags:
- data
- math
- MER
size_categories:
- 1M<n<10M
---
# UniMER Dataset

For detailed instructions on using the dataset, please refer to the project homepage: [UniMERNet Homepage](https://github.com/opendatalab/UniMERNet/tree/main)

## Introduction
The UniMER dataset is a specialized collection curated to advance the field of Mathematical Expression Recognition (MER). It encompasses the comprehensive UniMER-1M training set, featuring over one million instances that represent a diverse and intricate range of mathematical expressions, coupled with the UniMER Test Set, meticulously designed to benchmark MER models against real-world scenarios. The dataset details are as follows:

- **UniMER-1M Training Set:**
  - Total Samples: 1,061,791 Latex-Image pairs
  - Composition: A balanced mix of concise and complex, extended formula expressions
  - Aim: To train robust, high-accuracy MER models, enhancing recognition precision and generalization

- **UniMER Test Set:**
  - Total Samples: 23,757, categorized into four types of expressions:
    - Simple Printed Expressions (SPE): 6,762 samples
    - Complex Printed Expressions (CPE): 5,921 samples
    - Screen Capture Expressions (SCE): 4,742 samples
    - Handwritten Expressions (HWE): 6,332 samples
  - Purpose: To provide a thorough evaluation of MER models across a spectrum of real-world conditions


## Visual Data Samples
![UniMER-Test](https://github.com/opendatalab/UniMERNet/assets/69186975/7301df68-e14c-4607-81bc-b6ee3ba1780b)
  
## Data Statistics
  | **Dataset** | **Sub** |                  **Source**                 | **Sample Size** |
  |:-----------:|:-------:|:-------------------------------------------:|:---------------:|
  |  UniMER-1M  |         |                   Pix2tex Train             |      158,303    | 
  |             |         |                    Arxiv †                  |      820,152    | 
  |             |         |                 CROHME Train                |       8,834     | 
  |             |         |                 HME100K Train ‡             |      74,502     |  
  | UniMER-Test |   SPE   |                 Pix2tex Validation          |       6,762     | 
  |             |   CPE   |                    Arxiv †                  |       5,921     |
  |             |   SCE   |                PDF Screenshot †             |       4,742     |
  |             |   HWE   |               CROHME & HME100K              |       6,332     |

† Indicates data collected, processed, and annotated by our team.  
‡ For copyright compliance, please manually download this dataset portion: [HME100K dataset](https://ai.100tal.com/dataset).

## Acknowledgements
We would like to express our gratitude to the creators of the [Pix2tex](https://github.com/lukas-blecher/LaTeX-OCR), [CROHME](https://www.cs.rit.edu/~rlaz/files/CROHME+TFD%E2%80%932019.pdf), and [HME100K](https://github.com/tal-tech/SAN) datasets. Their foundational work has significantly contributed to the development of the UniMER dataset. 


## Citations

```text
@misc{wang2024unimernet,
      title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, 
      author={Bin Wang and Zhuangcheng Gu and Chao Xu and Bo Zhang and Botian Shi and Conghui He},
      year={2024},
      eprint={2404.15254},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{conghui2022opendatalab,
    author={He, Conghui and Li, Wei and Jin, Zhenjiang and Wang, Bin and Xu, Chao and Lin, Dahua},
    title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets},
    howpublished = {\url{https://opendatalab.com}},
    year={2022}
}
```

---

# UniMER 数据集

数据集使用详细说明请参考项目主页：[UniMERNet 主页](https://github.com/opendatalab/UniMERNet/tree/main)

## 简介
UniMER数据集是专门为通用数学表达式识别（MER）发布的数据集。它包含了真实全面的UniMER-1M训练集，拥有超过一百万个代表广泛和复杂数学表达式的实例，以及精心设计的UniMER测试集，用于在真实世界场景中评估MER模型。数据集详情如下：

- **UniMER-1M 训练集：**
  - 总样本数：1,061,791
  - 组成：简洁与复杂、扩展公式表达式的平衡融合
  - 目标：帮助训练鲁棒性强、高精度的MER模型，增强识别准确性和模型泛化能力

- **UniMER 测试集：**
  - 总样本数：23,757，分为四种表达式类型：
    - 简单印刷表达式（SPE）：6,762 个样本
    - 复杂印刷表达式（CPE）：5,921 个样本
    - 屏幕截图表达式（SCE）：4,742 个样本
    - 手写表达式（HWE）：6,332 个样本
  - 目的：为MER模型提供一个全面的评估平台，以准确评估真实场景下各类公式识别能力

## 视觉数据样本
![UniMER-测试集](https://github.com/opendatalab/UniMERNet/assets/69186975/7301df68-e14c-4607-81bc-b6ee3ba1780b)
  
## 数据统计
  | **数据集** | **子集** |                  **来源**                   | **样本数量** |
  |:-----------:|:-------:|:-------------------------------------------:|:------------:|
  |  UniMER-1M  |         |                   Pix2tex 训练集             |    158,303   | 
  |             |         |                    Arxiv †                  |    820,152   | 
  |             |         |                 CROHME 训练集                |      8,834   | 
  |             |         |                 HME100K 训练集 ‡             |     74,502   |  
  | UniMER-测试集 |   SPE   |                 Pix2tex 验证集              |      6,762   | 
  |             |   CPE   |                    Arxiv †                  |      5,921   |
  |             |   SCE   |                PDF 截图 †                    |      4,742  |
  |             |   HWE   |              CROHME & HME100K               |      6,332  |

† 表示由我们团队收集、处理和注释的数据。  
‡ 由于版权合规，请手动下载此部分数据集：[HME100K 数据集](https://ai.100tal.com/dataset)。

## 致谢
我们对[Pix2tex](https://github.com/lukas-blecher/LaTeX-OCR), [CROHME](https://www.cs.rit.edu/~rlaz/files/CROHME+TFD%E2%80%932019.pdf)和[HME100K](https://github.com/tal-tech/SAN) 数据集的创建者表示感谢。他们的基础工作对 UniMER 数据集的构建及发布做出了重大贡献。

## 引用
```text
@misc{wang2024unimernet,
      title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, 
      author={Bin Wang and Zhuangcheng Gu and Chao Xu and Bo Zhang and Botian Shi and Conghui He},
      year={2024},
      eprint={2404.15254},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{conghui2022opendatalab,
    author={He, Conghui and Li, Wei and Jin, Zhenjiang and Wang, Bin and Xu, Chao and Lin, Dahua},
    title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets},
    howpublished = {\url{https://opendatalab.com}},
    year={2022}
}
```