Intelligent Vehicle Number Plate Extraction and Recognition from Traffic Images

Abstract

The rapid increase in the number of vehicles on urban roads has created a strong demand for automated traffic monitoring and vehicle identification systems. This paper presents an intelligent approach for extracting and recognizing vehicle number plates from traffic images captured under different environmental conditions. The proposed system combines image preprocessing, plate localization, character segmentation, and optical character recognition techniques to identify registration numbers accurately from static traffic images. Initially, the input image is enhanced using noise reduction and contrast adjustment methods to improve visual quality. Edge and contour analysis are then applied to detect the probable number plate region, followed by segmentation of individual characters from the extracted plate. Finally, a recognition module interprets the segmented characters and converts them into machinereadable text. The system is designed to handle variations in lighting, vehicle orientation, image resolution, and background complexity, making it suitable for real-world traffic surveillance applications. Experimental analysis demonstrates that the proposed approach can achieve reliable number plate detection and recognition with improved accuracy and reduced processing time. The developed framework can be effectively used in traffic law enforcement, automated toll collection, parking management, vehicle tracking, and smart transportation systems. Keywords: License Plate Extraction, Traffic Image Processing, Optical Character Recognition, Image Segmentation, Edge Detection, Intelligent Transportation Systems, Vehicle Identification, Automated Traffic Monitoring.