README.md · 0xnu/european-license-plate-recognition at main

metadata

license: mit
datasets:
  - 0xnu/european-licence-plate
tags:
  - eu
  - european-union
  - transport
  - transportation
  - computer-vision
  - object-detection
  - license-plate-recognition
  - ocr
language:
  - en
  - de
  - fr
  - es
  - it
  - nl

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}}
}

0xnu
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european-license-plate-recognition