Datasets:

wanderkid
/

UniMER_Dataset

	---
	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}
	}
	```