Automatic License Plate Recognition System for Vehicles Using a CNN

Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These play a significant role border surveillance, ensuring safeguards, handling vehicle-related crime. The most approach for implementing ALPR utilizes deep learning via convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the differentiating them from each other. CNNs popular license plate character recognition. However, little has been reported results these with regard unusual varieties plates or their success at night. We present efficient system that uses combination pre-processing morphological operations was applied enhance quality, which aids efficiency. features, such ability recognize multi-line, skewed, multi-font plates. It also efficiently night mode can be used different vehicle types. An overall accuracy 98.13% achieved using proposed technique.