A Typical Neural Network-Based Automated System for Number Plate Recognition and Detection

Because of its various real-world uses, automatic registration number plate recognition, or ALPR, has become a common subject of research. Nevertheless, a lot of the existing solutions-which frequently rely on numerous constraintsare not yet reliable in practical settings. A reliable and effective ALPR system built on the cutting-edge YOLO object detector is presented in this research. For every ALPR stage, CNNs are developed and adjusted to make them resilient to various circumstances. We develop a two-stage method specifically for character division and recognition, using straightforward data augmentation techniques like flipped characters and reversed license plates (LPs).