european-license-plate-recognition

EULPR: European License Plate Recognition

EULPR is a computer-vision model architecture purpose-built for detecting, reading, and recognizing European license plates. It is optimized for speed and accuracy across diverse EU plate formats.

Model Performance

• Detection Rate: 100.0%
• Text Extraction Rate: 100.0%
• Processing Speed: 7.6 FPS
• Model Size: YOLOv12 Nano (~10.5MB)

Supported Languages

• English (en)
• German (de)
• French (fr)
• Spanish (es)
• Italian (it)
• Dutch (nl)

Quick Start

Installation

pip install ultralytics easyocr opencv-python pillow torch torchvision huggingface_hub

Usage

import cv2
import numpy as np
from ultralytics import YOLO
import easyocr
from PIL import Image
from huggingface_hub import hf_hub_download
import warnings

# Suppress warnings
warnings.filterwarnings('ignore')

# Download models from HuggingFace
print("Downloading model from HuggingFace...")
model_path = hf_hub_download(repo_id="0xnu/european-license-plate-recognition", filename="model.onnx")
config_path = hf_hub_download(repo_id="0xnu/european-license-plate-recognition", filename="config.json")

# Load models with explicit task specification
yolo_model = YOLO(model_path, task='detect')
ocr_reader = easyocr.Reader(['en', 'de', 'fr', 'es', 'it', 'nl'], gpu=False, verbose=False)

# Process image
def recognize_license_plate(image_path):
   # Load image
   image = cv2.imread(image_path)
   image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
   
   # Detect license plates
   results = yolo_model(image_rgb, conf=0.5, verbose=False)
   
   plates = []
   for result in results:
       boxes = result.boxes
       if boxes is not None:
           for box in boxes:
               # Get coordinates
               x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
               
               # Crop plate
               plate_crop = image_rgb[int(y1):int(y2), int(x1):int(x2)]
               
               # Extract text
               ocr_results = ocr_reader.readtext(plate_crop)
               if ocr_results:
                   text = ocr_results[0][1]
                   confidence = float(ocr_results[0][2])  # Convert to native Python float
                   plates.append({'text': text, 'confidence': confidence})
   
   return plates

# Usage Example
results = recognize_license_plate('sample_car_with_license.jpeg')
print(results)

Model Architecture

Detection Model (YOLOv12n)

• Architecture: YOLOv12 Nano
• Parameters: ~3M
• Input Size: 640x640 pixels
• Output: Bounding boxes for license plates

OCR Model (EasyOCR)

• Engine: Deep learning-based OCR
• Languages: Multi-European language support
• Character Set: Alphanumeric + common symbols

Training Details

• Dataset: European License Plate Dataset (0xnu/european-licence-plate)
• Training Epochs: 30
• Batch Size: 16
• Image Size: 640x640
• Optimizer: AdamW
• Framework: Ultralytics YOLOv12

Use Cases

• Traffic monitoring systems
• Automated parking management
• Law enforcement applications
• Toll collection systems
• Vehicle access control

Limitations

• Optimized for European license plate formats
• Performance may vary with extreme weather conditions
• Requires good image quality for optimal text recognition
• Real-time performance depends on hardware capabilities

License

This project is licensed under the Modified MIT License.

Citation

If you use this model in your research or product, please cite:

@misc{eulpr2025,
  title={EULPR: European License Plate Recognition},
  author={Finbarrs Oketunji},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/0xnu/european-license-plate-recognition}}
}

Copyright

Copyright (C) 2025 Finbarrs Oketunji. All Rights Reserved.