Indian Automatic License Plate Recognition — Computer Vision Solution
AI
Computer Vision
Deep Learning
Smart Cities
Transportation
TensorFlow
Overview
Developed a complete automatic license plate recognition (ALPR) solution optimized for Indian license plate formats. The system leverages advanced computer vision models to detect vehicle license plates from images and extract text through optical character recognition, enabling automated vehicle identification for smart city and transportation applications.
Key Responsibilities
- Model Architecture Development: Designed and implemented dual-model approach using YOLOv3 and RetinaNet for maximizing accuracy across different scenarios and lighting conditions
- Indian Dataset Optimization: Created specialized training pipeline optimized for Indian license plate formats, handling unique regional variations and character sets
- OCR Integration: Integrated Tesseract OCR engine with custom fine-tuning for improved character recognition accuracy on license plate text
- Real-time Processing: Optimized computer vision pipeline for sub-second processing times, enabling deployment in high-throughput applications
- Web Application Development: Built interactive Streamlit web interface for real-time testing, demonstration, and user-friendly model evaluation
- Containerization: Architected Docker-based deployment solution ensuring consistent performance across different environments
Technical Achievements
- Built dual-model ALPR system with YOLOv3 and RetinaNet integration
- Achieved sub-second recognition times with high accuracy
- Implemented Indian license plate format optimization
- Developed weather and lighting condition resilience features
- Created interactive web interface with Streamlit
- Enabled Docker containerization for production deployment
Impact
Revolutionized automated vehicle identification for smart city applications, enabling efficient toll collection, parking management, and security surveillance. Demonstrated practical computer vision implementation that achieved high accuracy results while establishing patterns for real-time computer vision systems in transportation and security domains.