Automated Parking Management in Urban Environment Using Deep Learning and OCR Technique

The increasing demand for parking in cities calls for a proper management system to keep congestion under control while ensuring ease of use for the users. This paper describes an automated parking solution based on combining deep learning and Optical Character Recognition (OCR) for optimizing space utilization and simplifying vehicle identification. Convolutional Neural Networks (CNNs) are used for object detection in real time by means of surveillance cameras for vacant and occupied parking space detection and vehicle classification. OCR technology is utilized to read and process license plate information for automated entry-exit tracking, permit validation, and billing. The cloud-based platform, along with data storage and analytics, allows the administrators access to insightful information regarding the occupancy trend for efficient space allocation. The proposed solution has been demonstrated to maintain high accuracy even in harsh conditions like varying lighting conditions and motion blur. Processing information in real time keeps latency to a minimum, aiming at presenting parking availability almost immediately across digital signage and mobile applications. Thus, by automating parking assignments, AI integrated with deep learning and OCR will help in monitoring and enforcement, providing a seamless experience for users. This scalable solution can be applied in various urban settings, furthering the evolution of smart cities supported by efficient technology-driven parking infrastructure.