Application of Optical Character Recognizer Prototype and Convulsion Neural Network for Vehicle License Plate Detection

Abstract

License plates play an important role in vehicle identification in a variety of applications such as traffic safety, parking management, and traffic enforcement. In this study, we propose the development of license plate recognition applications using optical character recognition (OCR) and convolutional neural network (CNN) techniques. The OCR method is used to recognize characters on license plates and the CNN method is used to recognize license plates in images. The purpose of this research is to develop a system that can automatically recognize and recognize license plates in images. The OCR method accurately recognizes the characters on the license plate. Additionally, the CNN method is employed to detect license plates with good accuracy, even in various formats of license plates. The proposed methods in this research are implemented in the form of an application using the Python programming language. The application takes vehicle images as input and produces text recognition of the license plate as output. Furthermore, the application can also display additional information such as date, time, location, and detected vehicle type. Through this research, it is expected that the license plate recognition application using OCR and CNN methods can contribute to improving efficiency and reliability in automatic license plate recognition. Moreover, this application also has the potential to be used in various security applications, traffic monitoring, and automatic vehicle recognition