Paper Title
Real-Time Intelligent Vehicle Monitoring and License Plate Recognition System Using Yolov8 and Easyocr with Gui-Based Logging

Abstract
This research presents a comprehensive, real-time system for automated vehicle detection, classification, and license plate recognition leveraging state-of-the-art deep learning technologies. By integrating YOLOv8 for vehicle detection and EasyOCR for optical character recognition, the system achieves high accuracy and operational efficiency even in resource-constrained environments like educational campuses and gated communities. It features a robust GUI developed using Tkinter for real-time monitoring, event logging, and entry/exit tracking. Additionally, it supports live video feed processing, vehicle type identification, snapshot storage, and Excel log export, making it an effective solution for intelligent vehicular surveillance and traffic automation. The architecture is modular, supporting future extensions such as vehicle make model recognition and hardware acceleration with low-cost computing platforms like Raspberry Pi. The proposed solution is scalable and efficient, making it suitable for smart cities, automated parking, and law enforcement applications. Keywords - Vehicle detection, License plate recognition, YOLOv8, EasyOCR, Real-time monitoring, Traffic automation, Deep learning, Smart surveillance, GUI, Edge computing