Smart Video Number Plate Character Recognition and Speed measurement using Hybrid Optimization-based YoloV3

Abstract

In the present growing era of vehicular technology, number plate recognition is the prim in order to solve the multi-level problems of security. The Number Plate Recognition (NPR) using hybrid techniques (image processing + Optical Character Recognition (OCR)) is utmost important in design of security system, that automatically read and recognize the characters on a vehicle's number plate. The application areas considered are toll roads, parking areas, and other restricted zones. The parameters used for detection and validation are accuracy, precision recall and F1 score. The NPR system begins by capturing an image of the number plate using a camera or other imaging device. Then, the image is processed using image processing techniques to enhance the quality of the image and identify the number plate's location. Next, the OCR algorithm is applied to the image to read and recognize each character on the number plate accurately. The accuracy of the NPR system depends on the quality of the captured image and the efficiency of the OCR algorithm. According to the research papers, the NPR system's accuracy in recognizing Indian number plates is between 75-85%. For high speed vehicles Optimized YOLOV3 is used for detecting the number plates. Once the number plates are detected, character recognition can be performed using the Improved Convolutional Neural Network (ICNN). The NPR system has several practical applications in the transportation industry, law enforcement agencies, and parking management systems. The system can help automate toll collection, improve traffic flow, and enhance the security of restricted areas by identifying and tracking vehicles. Overall, the NPR system is an essential technology that can improve the efficiency and security of various transportation-related operations. Its effectiveness and accuracy make it a valuable tool for various industries and organizations.