Published Paper Details:

Automatic License Plate Recognition (ALPR) is an advanced computer vision technology that extracts vehicle registration numbers from images and videos for various applications, including security, traffic management, and automated parking systems. This research focuses on developing an efficient and accurate ALPR system leveraging Optical Character Recognition (OCR) and Fuzzy Searching techniques to enhance accuracy, even with noisy or unclear images. The system follows a structured approach where license plate images are first captured through a camera, processed using OCR to extract alphanumeric characters, and further refined with fuzzy searching algorithms to handle variations caused by lighting, font differences, and image distortions. The extracted license number is then matched against a database to identify the vehicle owner and generate real-time reports for security personnel, administrators, or authorities. A key contribution of this research is the integration of real-time analytics and a user-friendly dashboard for monitoring visitor logs, security access, and traffic trends. The proposed model is tested on a dataset of license plate images under varying environmental conditions to evaluate performance in terms of accuracy, processing time, and robustness. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to traditional OCR-based models, particularly in challenging scenarios. This work has broad applications in smart parking systems, automated toll collection, vehicle entry management in organizations, and traffic surveillance. The future scope includes integrating deep learning-based recognition methods, expanding the dataset to accommodate multiple regional plate formats, and enhancing security features with blockchain-based data integrity.