UK ANPR
Automatic Number Plate Recognition for UK Number Plates
We developed a high-accuracy Automatic Number Plate Recognition (ANPR) system specifically designed for UK number plates. The solution leverages YOLOv8 segmentation and CNN-based character recognition to achieve precise alphanumeric extraction while validating dimensional compliance with UK regulatory standards.
What We Built
A specialized ANPR ecosystem using:
- YOLOv8 Segmentation for detecting and segmenting multi-character UK number plates
- CNN for Character Recognition for learning detailed alphanumeric features
- OpenCV preprocessing for brightness, contrast, and angle correction
- Dimensional validation engine for checking character height, width, and spacing
- Background color recognition for plate type classification
- Real-time video processing capability for live monitoring applications
The system acts as a forensic-grade ANPR solution, dynamically analyzing both visual content and physical layout for unmatched accuracy and regulatory compliance.
Key Features
Adv. Character Segmentation
- YOLOv8-based segmentation for individual character isolation
- Efficient detection under challenging backgrounds and lighting
- High accuracy in multi-character targets
Dimensional Validation
- Strict validation of character height, width, and spacing
- Compliance with UK regulatory standards
- Detection of spacing differences down to 0.01 mm
Background Color Recognition
- Differentiation of number plate backgrounds
- Accurate plate identification despite color variations
- Supports UK plate standards compliance
Real-Time Processing
- Near-instantaneous number plate recognition
- Suitable for live monitoring and enforcement
- Handles varying distances, angles, and lighting conditions
How It Works
Images or video frames are ingested into the system
YOLOv8 detects and segments the number plate region
CNN extracts and recognizes individual alphanumeric characters
Dimensional validation checks height, width, and spacing against UK standards
Background color is classified for plate type verification
Structured output provides registration number and dimensional profile
Results
95%+
accuracy in plate recognition
91%
accuracy in character dimension and spacing validation
Real-time
processing speed for live monitoring applications
Reliable
performance across varied lighting, angles, and vehicle types
Value Dilevered
This project transforms traditional ANPR with a forensic layer of geometric analysis. Beyond reading plates, the system validates the physical layout of characters making it invaluable for detecting cloned or fraudulent plates, managing automated tolling and access control, and enforcing UK traffic regulations with high confidence.
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#Computer Vision #OCR #ANPR #YOLOv8 #Image Segmentation #UK Traffic Standards #Intelligent Transportation Systems #Python