|
# 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 |
|
![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} |
|
} |
|
``` |
|
|
|
--- |
|
|
|
# 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 |
|
![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} |
|
} |
|
``` |
|
|