fastplateocr-py 0.1.0

Accurate and Efficient General OCR System for License Plates

FastPlateOCR

🚀 FastPlateOCR is an accurate, extremely fast, and flexible End-to-End License Plate Recognition library.

This project is deeply inspired by and based on the excellent OpenOCR framework. We have specialized and optimized the architecture specifically for reading License Plates in challenging real-world conditions.

By integrating the lightweight object detection prowess of YOLO26 (with RepMixer Bottlenecks) and the lightning-fast text recognition of SVTRv2, FastPlateOCR delivers production-ready performance!

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🛠 Installation

pip install fastplateocr-py

(Note: To use the auto-download feature for pre-trained weights, please ensure huggingface_hub is installed).

⚡ Quick Start

FastPlateOCR automatically downloads the best pre-trained models from our HuggingFace repository the first time you run it. You don't need to manually configure any paths!

1. End-to-End Recognition (Detect & Read)

import cv2
from fastplateocr import FastPlateOCR

# Initialize (auto-downloads weights if not found)
model = FastPlateOCR()

# Read the plate
results = model.read('car_image.jpg')

for res in results:
    print(f"Plate Text: {res['text']} | Confidence: {res['score']:.4f}")
    print(f"Bounding Box: {res['box']}")

2. Flexible API: Detect Only

If you only need to locate the license plates without reading the text:

# Disable the recognition model to save memory
model = FastPlateOCR(use_rec=False)
boxes = model.detect('car_image.jpg')
print("Detected boxes:", boxes)

3. Flexible API: Recognize Only

If you already have a cropped image of a license plate and just want to read the characters:

# Disable the detection model
model = FastPlateOCR(use_det=False)

crop_img = cv2.imread('cropped_plate.jpg')
text, score = model.recognize(crop_img)
print(f"Text: {text} (Score: {score})")

4. Using Custom Local Weights

If you have fine-tuned your own models or downloaded the weights locally, you can easily load them:

model = FastPlateOCR(
    det_model_path="/path/to/your/yolo.pt",
    rec_model_path="/path/to/your/svtr.pth"
)

🏋️ Training & Evaluation

FastPlateOCR provides a complete suite of scripts in the tools/ directory for dataset preparation, training, evaluation, and inference.

0. Model Weights Preparation

Before training or evaluation, download the official pre-trained models from our HuggingFace Repository and place them in the following structure:

FastPlateOCR/
├── pretrained_models/
│   ├── yolo26n_rep_mixer/
│   │   └── best.pt
│   └── svtrv2_tiny_efficient_rctc/
│       └── best.pth

You can download them manually or use wget:

wget -O pretrained_models/det/yolo26n_rep_mixer/best.pt https://huggingface.co/anhone3/FastPlateOCR/resolve/main/yolo26n_rep_mixer/best.pt
wget -O pretrained_models/rec/svtrv2_tiny_efficient_rctc/best.pth https://huggingface.co/anhone3/FastPlateOCR/resolve/main/svtrv2_tiny_efficient_rctc/best.pth

1. Data Preparation (Create LMDB)

Because our LMDB script uses hardcoded paths for simplicity, please open tools/create_lmdb_dataset.py and modify the data_dir variable in the __main__ block to match your dataset path before running:

if __name__ == '__main__':
    data_dir = './dataset/rec' # Set your dataset directory

    label_file_list = [
        os.path.join(data_dir, 'train_labels.txt'),
        os.path.join(data_dir, 'val_labels.txt'),
        os.path.join(data_dir, 'test_labels.txt')
    ]

After modifying the paths, generate the LMDB:

python tools/create_lmdb_dataset.py

2. Training (Det & Rec)

Before training, you must configure the dataset paths, batch sizes, and learning rates in the respective .yml files.

For Detection (configs/det/yolo26/yolo26n_rep_mixer.yml):

Train:
  data: './dataset/det/data.yaml' # Point this to your YOLO data.yaml
  epochs: 50
  batch: 256

For Recognition (configs/rec/svtrv2/svtrv2_tiny_efficient_rctc.yml):

Train:
  dataset:
    name: RatioDataSetTVResize
    data_dir_list: ['./dataset/rec/lmdb_data/train']

Eval:
  dataset:
    name: RatioDataSetTVResize
    data_dir_list: ['./dataset/rec/lmdb_data/val']

Once configured, start training:

# Train Detection Model (YOLO26)
python tools/train_det.py -c configs/det/yolo26/yolo26n_rep_mixer.yml

# Train Recognition Model (SVTRv2)
python tools/train_rec.py -c configs/rec/svtrv2/svtrv2_tiny_efficient_rctc.yml

3. Evaluation (Validation)

Evaluate your trained checkpoints on the validation set using the config files:

# Evaluate Detection
python tools/eval_det.py -c configs/det/yolo26/yolo26n_rep_mixer.yml

# Evaluate Recognition
python tools/eval_rec.py -c configs/rec/svtrv2/svtrv2_tiny_efficient_rctc.yml

4. Batch Inference

Test your checkpoints directly on directories of images (supports --save_log to save predictions):

# Infer Detection
python tools/infer_det.py -m pretrained_models/det/yolo26n_rep_mixer/best.pt -d dataset/det/test/images --save_log

# Infer Recognition
python tools/infer_rec.py -m pretrained_models/rec/svtrv2_tiny_efficient_rctc/best.pth -d dataset/rec/test --save_log

🤝 Acknowledgements

• OpenOCR: FastPlateOCR is built upon the robust foundation of OpenOCR.
• YOLO26 & SVTRv2: This work heavily leverages the architectural innovations from YOLO26 for high-speed object detection and SVTRv2 for accurate text recognition.
  • Read the YOLO26 Paper
  • Read the SVTRv2 Paper

📧 Contact

For any questions or issues, please open an issue or contact: [e-mail].