Optimizing Deep Neural Network for Automatic Number Plate Recognition in Challenging Environment

The study of automatic number plate recognition, a subfield of computer vision and machine learning, has grown in importance. Parking management, toll collection, and traffic monitoring are just a few of the many uses for automatic number plate recognition devices. However, automatic number plate recognition in difficult situations like dim lighting, bad image quality, and occlusions is still a difficult task. In this study, using the TensorFlow and EasyOCR libraries, a novel deep neural network architecture is suggested for automatic number plate recognition in difficult environments. First, examination of the different automatic number plate recognition challenges and how they affect the effectiveness of the current automatic number plate recognition systems. It is then suggested that a deep neural network design should be used to boost the automatic number plate recognition systems’ recognition accuracy in difficult environments by combining convolutional and recurrent layers. Overall, this study suggests new deep neural network architecture for Automatic Number Plate Recognition (automatic number plate recognition) in difficult environments. To increase recognition accuracy, the proposed architecture combines convolutional and recurrent layers, including a kind of recurrent neural network (RNN) dubbed long short-term memory (LSTM). On an openly accessible dataset, the system’s accuracy was 91% and it was developed using the libraries TensorFlow and EasyOCR. The results of this study could be used in a number of industries, including law enforcement, transportation, and parking administration.