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# UniMER Dataset
## 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): 4,774 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
## 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
## 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）：4,774 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
## 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}
}
``` |