Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- 0xnu/uk-licence-plate
+tags:
+- uk
+- united-kingdom
+- transport
+- transportation
+- computer-vision
+- object-detection
+- license-plate-recognition
+- ocr
+language:
+- en
+---
+
+## UKLPR: United Kingdom License Plate Recognition
+
+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.
+
+### Model Performance
+
+- **Detection Rate**: 100.0%
+- **Text Extraction Rate**: 100.0%
+- **Processing Speed**: 8.1 FPS
+- **Model Size**: YOLOv8 Nano (~12.3MB)
+
+### Supported Languages
+
+- English (en)
+
+### Quick Start
+
+#### Installation
+
+```python
+pip install ultralytics easyocr opencv-python pillow torch torchvision huggingface_hub
+```
+
+#### Usage
+
+```python
+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/uk-license-plate-recognition", filename="model.onnx")
+config_path = hf_hub_download(repo_id="0xnu/uk-license-plate-recognition", filename="config.json")
+
+# Load models with explicit task specification
+yolo_model = YOLO(model_path, task='detect')
+ocr_reader = easyocr.Reader(['en'], 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 (YOLOv8n)
+- **Architecture**: YOLOv8 Nano
+- **Parameters**: ~3M
+- **Input Size**: 640x640 pixels
+- **Output**: Bounding boxes for license plates
+
+#### OCR Model (EasyOCR)
+- **Engine**: Deep learning-based OCR
+- **Languages**: English
+- **Character Set**: Alphanumeric + common symbols
+
+### Training Details
+
+- **Dataset**: UK License Plate Dataset ([0xnu/uk-licence-plate](https://huggingface.co/datasets/0xnu/uk-licence-plate))
+- **Training Epochs**: 10
+- **Batch Size**: 16
+- **Image Size**: 640x640
+- **Optimizer**: AdamW
+- **Framework**: Ultralytics YOLOv8
+
+### Use Cases
+
+- Traffic monitoring systems
+- Automated parking management
+- Law enforcement applications
+- Toll collection systems
+- Vehicle access control
+
+### Limitations
+
+- Optimized for United Kingdom 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](./LICENSE).
+
+### Citation
+
+If you use this model in your research or product, please cite:
+
+```bibtex
+@misc{uklpr2025,
+  title={UKLPR: United Kingdom License Plate Recognition},
+  author={Finbarrs Oketunji},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/0xnu/uk-license-plate-recognition}}
+}
+```
+
+### Copyright
+
+Copyright (C) 2025 Finbarrs Oketunji. All Rights Reserved.