Vehicle Number Plate Detection With State Vehicle tracking Analysis System

Abstract

Automatic vehicle number plate detection has emerged as a critical component in modern intelligent transportation systems, playing a vital role in traffic management, security surveillance, and law enforcement. This paper presents a comprehensive system for automatic vehicle number plate detection integrated with state-level traffic analysis capabilities. The proposed system utilizes advanced image processing techniques combined with machine learning algorithms to achieve robust number plate recognition under diverse environmental conditions. The system employs a multi-stage approach including vehicle detection, license plate localization, character segmentation, and optical character recognition (OCR). Additionally, the integration of state-wise traffic analysis enables real-time monitoring of vehicle movement patterns, inter-state traffic flow analysis, and generation of comprehensive traffic statistics. Experimental results demonstrate that the proposed system achieves an accuracy of 96.8% in number plate detection across various lighting conditions, weather scenarios, and plate formats. The state traffic analysis module successfully processes and categorizes vehicles based on their registered states, enabling efficient traffic management and violation detection. The system has been tested with a dataset of 5,000 vehicle images captured under real-world conditions, showing promising results for deployment in smart city infrastructure and highway toll management systems.