Upload UniMER-1M and UniMER-Test Dataset

README.md

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----
-license: apache-2.0
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+# 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}
+}
+```

UniMER-1M.zip

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UniMER-test.zip

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