unimernet

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UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition

[ Paper ] [ Website ] [ Dataset (OpenDataLab)] [ Dataset (Hugging Face) ]

[Models 🤗(Hugging Face)] [Models (ModelScope)]

🔥🔥 CDM: A Reliable Metric for Fair and Accurate Formula Recognition Evaluation

Welcome to the official repository of UniMERNet, a solution that converts images of mathematical expressions into LaTeX, suitable for a wide range of real-world scenarios.

News 🚀🚀🚀

2024.09.06 🎉🎉 UniMERNet Update: The new version features a smaller model and faster inference. Training code is now open-sourced. For details, see the latest paper UniMERNet.
2024.09.06 🎉🎉 Introducing a new metric for formula recognition: CDM. Compared to BLEU/EditDistance, CDM provides a more intuitive and accurate evaluation score, allowing for fair comparison of different models without being affected by formula expression diversity.
2024.07.21 🎉🎉 Add Math Formula Detection (MFD) Tutorial based on PDF-Extract-Kit MFD model.
2024.06.06 🎉🎉 Open-sourced evaluation code for UniMER dataset.
2024.05.06 🎉🎉 Open-sourced UniMER dataset, including UniMER-1M for model training and UniMER-Test for MER evaluation.
2024.05.06 🎉🎉 Add Streamlit formula recognition demo and provided local deployment App.
2024.04.24 🎉🎉 Paper now available on ArXiv.
2024.04.24 🎉🎉 Inference code and checkpoints have been released.

Demo Video

https://github.com/opendatalab/UniMERNet/assets/69186975/ac54c6b9-442c-48b0-95f9-a4a3fce8780b

https://github.com/opendatalab/UniMERNet/assets/69186975/09b71c55-c58a-4792-afc1-d5774880ccf8

Quick Start

Clone the repo and download the model

git clone https://github.com/opendatalab/UniMERNet.git

cd UniMERNet/models
# Download the model and tokenizer individually or use git-lfs
git lfs install
git clone https://huggingface.co/wanderkid/unimernet_base  # 1.3GB  
git clone https://huggingface.co/wanderkid/unimernet_small # 773MB  
git clone https://huggingface.co/wanderkid/unimernet_tiny  # 441MB  

# you can also download the model from ModelScope
git clone https://www.modelscope.cn/wanderkid/unimernet_base.git
git clone https://www.modelscope.cn/wanderkid/unimernet_small.git
git clone https://www.modelscope.cn/wanderkid/unimernet_tiny.git

Installation

Create a clean Conda environment

conda create -n unimernet python=3.10
conda activate unimernet

Method 1: Install via pip (recommended for general users)

pip install -U "unimernet[full]"

Method 2: Local installation (recommended for developers)

pip install -e ."[full]"

Running UniMERNet

1. Streamlit Application: For an interactive and user-friendly experience, use our Streamlit-based GUI. This application allows real-time formula recognition and rendering.

   unimernet_gui
   

   Ensure you have the latest version of UniMERNet installed (pip install --upgrade unimernet & pip install "unimernet[full]") for the streamlit GUI application.

2. Command-line Demo: Predict LaTeX code from an image.

   python demo.py
   

3. Jupyter Notebook Demo: Recognize and render formula from an image.

   jupyter-lab ./demo.ipynb
   

Performance Comparison (BLEU) with SOTA Methods.

UniMERNet significantly outperforms mainstream models in recognizing real-world mathematical expressions, demonstrating superior performance across Simple Printed Expressions (SPE), Complex Printed Expressions (CPE), Screen-Captured Expressions (SCE), and Handwritten Expressions (HWE), as evidenced by the comparative BLEU Score evaluation.

Visualization Result with Different Methods.

UniMERNet excels in visual recognition of challenging samples, outperforming other methods.

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): 4,742 samples
  • Handwritten Expressions (HWE): 6,332 samples
• Purpose: To provide a thorough evaluation of MER models across a spectrum of real-world conditions

Dataset Download

You can download the dataset from OpenDataLab (recommended for users in China) or HuggingFace.

Download UniMER-Test Dataset

Download the UniMER-1M dataset and extract it to the following directory:

./data/UniMER-1M

Download the UniMER-Test dataset and extract it to the following directory:

./data/UniMER-Test

Training

To train the UniMERNet model, follow these steps:

1. Specify the Training Dataset Path: Open the configs/train fold and set the path to your training dataset.

2. Run the Training Script: Execute the following command to start the training process.

   bash script/train.sh
   

Notes:

• Ensure that the dataset path specified in the configs/train fold is correct and accessible.
• Monitor the training process for any errors or issues.

Testing

To test the UniMERNet model, follow these steps:

1. Specify the Test Dataset Path: Open the configs/val fold and set the path to your test dataset.

2. Run the Test Script: Execute the following command to start the testing process.

   bash script/test.sh
   

Notes:

• Ensure that the dataset path specified in the configs/val fold is correct and accessible.
• The test.py script will use the specified test dataset for evaluation. Remember to change the test set path in test.py to your actual path.
• Review the test results for performance metrics and any potential issues.

Math Formula Detection Tutorial

The prerequisite for formula recognition is to detect the areas within PDF or webpage screenshots where formulas are located. The PDF-Extract-Kit includes a powerful model for detecting formulas. If you wish to perform both formula detection and recognition by yourself, you can refer to the Formula Detection Tutorial for guidance on deploying and using the formula detection model.

TODO

[✅] Release inference code and checkpoints of UniMERNet.
[✅] Release UniMER-1M and UniMER-Test.
[✅] Open-source the Streamlit formula recognition GUI application.
[✅] Release the training code for UniMERNet.

Citation

If you find our models / code / papers useful in your research, please consider giving us a star ⭐ and citing our work 📝, thank you :)

@misc{wang2024unimernetuniversalnetworkrealworld,
      title={UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition}, 
      author={Bin Wang and Zhuangcheng Gu and Guang Liang and Chao Xu and Bo Zhang and Botian Shi and Conghui He},
      year={2024},
      eprint={2404.15254},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.15254}, 
}

@misc{wang2024cdmreliablemetricfair,
      title={CDM: A Reliable Metric for Fair and Accurate Formula Recognition Evaluation}, 
      author={Bin Wang and Fan Wu and Linke Ouyang and Zhuangcheng Gu and Rui Zhang and Renqiu Xia and Bo Zhang and Conghui He},
      year={2024},
      eprint={2409.03643},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.03643}, 
}

Acknowledgements

• VIGC. The model framework is dependent on VIGC.
• Texify. A mainstream MER algorithm, UniMERNet data processing refers to Texify.
• Latex-OCR. Another mainstream MER algorithm.
• Donut. The UniMERNet's Transformer Encoder-Decoder are referenced from Donut.
• Nougat. The tokenizer uses Nougat.

Contact Us

If you have any questions, comments, or suggestions, please do not hesitate to contact us at [e-mail].

License

Apache License 2.0