International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
A CLOUD-BASED INTELLIGENT CAR PARKING SERVICE Sudhir Sharma1, Ms. Preet Kaur2 1Research
Scholar, Department of Computer Science and Engineering, CT Institute of Engineering, Management & Technology Jalandhar, Punjab 2Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering, Management & Technology Jalandhar, Punjab ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This study proposes a cloud-based intelligent
Recent advancements have seen the deployment of IoTenabled smart parking systems that utilize ANPR and OCR to automate vehicle identification and parking management. For instance, Dalarmelina et al. [1] introduced a real-time vehicle identification system leveraging OCR and wireless sensor networks, demonstrating effective management of parking spaces through intelligent transportation systems. Similarly, Paranjape et al. [2] developed a smart parking system employing image detection algorithms and OCR, facilitating automated entry and exit processes while maintaining accurate records of parking durations.
parking guidance system that leverages machine learning and the Internet of Things (IoT) to optimize parking space management in urban areas. Strategically placed sensors monitor real-time parking space availability, while a mobile app allows drivers to view, reserve, and navigate to available spots. The system incorporates cloud computing and Optical Character Recognition (OCR) to enhance security and efficiency. Cameras at entry and exit points capture vehicle license plates, which are processed by OCR algorithms and stored in a centralized cloud database. This enables automated tracking and accurate record-keeping of parking activity. The cloud infrastructure facilitates realtime data processing, remote access, and centralized management of multiple parking facilities. It supports advanced features such as booking systems, automated payments, and real-time updates on space availability, thereby improving user experience and operational efficiency. By automating vehicle identification and streamlining parking operations, the system reduces manual intervention, minimizes errors, and accelerates entry and exit processes. Overall, this smart parking solution enhances urban mobility by increasing parking efficiency, user convenience, and control over parking resources.
Moreover, the integration of deep learning techniques, such as Convolutional Neural Networks (CNNs), has enhanced the accuracy of license plate recognition under varying environmental conditions. A study by Sharma [3] proposed an automatic framework for number plate detection using OCR and deep learning approaches, aiming to improve detection performance. In another research, Fakhrurroja [4] explored automated license plate detection and recognition using YOLOv8 and OCR with a Tello drone camera, showcasing the potential of dronebased systems in parking management. Further advancements include the development of layoutindependent ALPR systems based on the YOLO detector, as presented by Laroca et al. [5], which achieved high recognition rates across multiple datasets. Additionally, Cai et al. [6] proposed a deep learning-based video system for accurate and real-time parking measurement, combining information across multiple image frames to enhance detection accuracy.
Key Words: ESP32 CAM, Node MCU 8266, Automatic License/Number Plate Recognition (ANPR), Optical Character Recognition (OCR), IR Sensor, Ultrasonic Sensor, Android Application.
1. INTRODUCTION The rapid urbanization and exponential growth in vehicle ownership have intensified the demand for efficient and intelligent parking solutions in metropolitan areas. Traditional parking systems often grapple with challenges such as manual ticketing, unauthorized access, and inefficient space utilization, leading to traffic congestion and user dissatisfaction. To address these issues, the integration of Internet of Things (IoT) technologies with advanced image processing techniques, particularly Automatic Number Plate Recognition (ANPR) and Optical Character Recognition (OCR), has emerged as a promising approach.
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Impact Factor value: 8.315
These innovations not only streamline parking operations but also contribute to the broader objectives of smart city initiatives by reducing manual interventions, optimizing space utilization, and providing real-time data analytics for better decision-making. The convergence of IoT, ANPR, and OCR technologies thus represents a significant step forward in developing intelligent parking solutions that cater to the evolving needs of urban mobility.
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