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--- |
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license: mit |
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datasets: |
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- 0xnu/uk-licence-plate |
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tags: |
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- uk |
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- united-kingdom |
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- transport |
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- transportation |
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- computer-vision |
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- object-detection |
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- license-plate-recognition |
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- ocr |
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language: |
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- en |
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--- |
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## UKLPR: United Kingdom License Plate Recognition |
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UKLPR is a computer-vision model architecture purpose-built for detecting, reading, and recognizing United Kingdom license plates. It is optimized for speed and accuracy across diverse UK plate formats. |
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### Model Performance |
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- **Detection Rate**: 100.0% |
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- **Text Extraction Rate**: 100.0% |
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- **Processing Speed**: 8.1 FPS |
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- **Model Size**: YOLOv8 Nano (~12.3MB) |
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### Supported Languages |
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- English (en) |
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### Quick Start |
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#### Installation |
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```python |
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pip install ultralytics easyocr opencv-python pillow torch torchvision huggingface_hub |
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``` |
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#### Usage |
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```python |
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import cv2 |
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import numpy as np |
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from ultralytics import YOLO |
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import easyocr |
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from PIL import Image |
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from huggingface_hub import hf_hub_download |
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import warnings |
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# Suppress warnings |
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warnings.filterwarnings('ignore') |
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# Download models from HuggingFace |
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print("Downloading model from HuggingFace...") |
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model_path = hf_hub_download(repo_id="0xnu/uk-license-plate-recognition", filename="model.onnx") |
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config_path = hf_hub_download(repo_id="0xnu/uk-license-plate-recognition", filename="config.json") |
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# Load models with explicit task specification |
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yolo_model = YOLO(model_path, task='detect') |
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ocr_reader = easyocr.Reader(['en'], gpu=False, verbose=False) |
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# Process image |
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def recognize_license_plate(image_path): |
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# Load image |
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image = cv2.imread(image_path) |
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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# Detect license plates |
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results = yolo_model(image_rgb, conf=0.5, verbose=False) |
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plates = [] |
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for result in results: |
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boxes = result.boxes |
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if boxes is not None: |
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for box in boxes: |
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# Get coordinates |
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy() |
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# Crop plate |
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plate_crop = image_rgb[int(y1):int(y2), int(x1):int(x2)] |
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# Extract text |
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ocr_results = ocr_reader.readtext(plate_crop) |
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if ocr_results: |
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text = ocr_results[0][1] |
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confidence = float(ocr_results[0][2]) # Convert to native Python float |
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plates.append({'text': text, 'confidence': confidence}) |
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return plates |
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# Usage Example |
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results = recognize_license_plate('sample_car_with_license.jpeg') |
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print(results) |
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``` |
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### Model Architecture |
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#### Detection Model (YOLOv8n) |
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- **Architecture**: YOLOv8 Nano |
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- **Parameters**: ~3M |
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- **Input Size**: 640x640 pixels |
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- **Output**: Bounding boxes for license plates |
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#### OCR Model (EasyOCR) |
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- **Engine**: Deep learning-based OCR |
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- **Languages**: English |
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- **Character Set**: Alphanumeric + common symbols |
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### Training Details |
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- **Dataset**: UK License Plate Dataset ([0xnu/uk-licence-plate](https://huggingface.co/datasets/0xnu/uk-licence-plate)) |
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- **Training Epochs**: 10 |
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- **Batch Size**: 16 |
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- **Image Size**: 640x640 |
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- **Optimizer**: AdamW |
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- **Framework**: Ultralytics YOLOv8 |
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### Use Cases |
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- Traffic monitoring systems |
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- Automated parking management |
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- Law enforcement applications |
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- Toll collection systems |
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- Vehicle access control |
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### Limitations |
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- Optimized for United Kingdom license plate formats |
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- Performance may vary with extreme weather conditions |
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- Requires good image quality for optimal text recognition |
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- Real-time performance depends on hardware capabilities |
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### License |
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This project is licensed under the [Modified MIT License](./LICENSE). |
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### Citation |
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If you use this model in your research or product, please cite: |
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```bibtex |
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@misc{uklpr2025, |
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title={UKLPR: United Kingdom License Plate Recognition}, |
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author={Finbarrs Oketunji}, |
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year={2025}, |
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publisher={Hugging Face}, |
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howpublished={\url{https://huggingface.co/0xnu/uk-license-plate-recognition}} |
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} |
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``` |
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### Copyright |
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Copyright (C) 2025 Finbarrs Oketunji. All Rights Reserved. |
