PaddleOCR License Plate Recognition Demo

Welcome to the License Plate Recognition demo project, powered by PaddleOCR!

This repository contains demo code for recognizing license plates, with detailed workflows and hands-on notebooks. The default environment targets Paperspace, but a Google Colab version is also available.

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🚀 Quick Start on Paperspace

1. Start a Jupyter notebook in Paperspace.

2. Clone this repository:

git clone https://github.com/chunchet-ng/paddleocr_lpr.git

3. Enter the project folder:

cd paddleocr_lpr

4. Set up the Conda environment:

bash conda.sh

5. Refresh your environment:

• Close all terminals and open a new one.
• Ensure the Conda base environment is activated.

6. Finalize setup:

bash setup.sh

7. In Jupyter, select the paddleocr_lpr kernel for all notebooks.

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🗂️ Code Structure

• Detection_Evaluation/HMean.ipynb
  Calculate HMean for text detection and spotting tasks (with step-by-step examples).

• Recognition_Evaluation/Acc_Edit_Distance.ipynb
  Compute accuracy and edit distance for text recognition tasks.

• License_Plate_Recognition/CCPD_2019.ipynb
  Explore and preprocess the CCPD 2019 dataset for license plate detection and recognition.

• License_Plate_Recognition/EAST_CRNN_LPR.ipynb
  Apply text detection (EAST), recognition (CRNN), and full license plate detection and recognition on CCPD 2019.

Note

Please run the CCPD_2019 notebook before starting with EAST_CRNN_LPR on your first use.

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📝 What You'll Learn

1. Text Detection & Spotting Evaluation:
   How to calculate HMean with practical examples.

2. Text Recognition Evaluation:
   Methods for computing accuracy, edit distance, and counting correctly recognized words.

3. Working with CCPD 2019:

   • Convert the CCPD 2019 dataset into PaddleOCR format.
   • Fine-tune and evaluate text detection (EAST) and recognition (CRNN) models.
   • Compare pre-trained and fine-tuned models using the provided evaluation scripts.

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📦 Dataset & Dependencies

• CCPD 2019 dataset:
  A large-scale, real-world Chinese city parking dataset for license plate detection and recognition.

• PaddleOCR:
  Powerful and easy-to-use OCR tools.

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🙏 Acknowledgements

• @nwjun: Special thanks for improving the notebooks and providing valuable feedback! 💪😇👍

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🤝 Contributing

Pull requests, bug reports, and suggestions are welcome! If you use this code or find it useful, please star the repo and share your results.

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