Vehicle Number Plate Recognition Using Raspberry Pi

Abstract

Vehicle number plate recognition (VNPR) systems have gained significant attention due to their wide range of applications in traffic management, parking enforcement, and security systems. This abstract presents an overview of a VNPR system that utilizes computer vision techniques and machine learning algorithms for license plate detection and recognition. The system is implemented using a Raspberry Pi platform, which provides a cost-effective and portable solution. The system follows a systematic approach to perform license plate recognition. Firstly, the camera captures images or video frames, which are then processed using the OpenCV library to enhance the visibility and quality of the license plates. License plate detection algorithms, employing edge detection or machine learning-based object detection techniques, are applied to locate and extract the license plate regions in the processed images. Following license plate detection, character segmentation techniques are utilized to isolate individual characters on the plate. Finally, integrated into the system, the Tesseract OCR engine recognizes the segmented characters and extracts the text information, effectively providing the license plate data.