Automated Parking Management with Real-Time Image Processing and OCR

Efficient parking management is a significant challenge in urban areas due to increasing vehicle density and limited space availability. This paper presents an automated parking management system that integrates real-time image processing and Optical Character Recognition (OCR) to detect vacant parking spaces and identify vehicle number plates, including both Bengali and English formats. The system utilizes deep learning models to address challenges such as low lighting, unstructured parking layouts, and diverse number plate formats. It is designed with a robust backend infrastructure, enabling real-time updates and parking space reservations. Tested in both simulated and real-world environments, the system achieves a parking space detection accuracy of 95.6% and a number plate recognition accuracy of 92.3%, with an average response time of 2.4 seconds for updating parking availability. This system provides a scalable and efficient solution for improving urban parking management.