Traditionally, the process of issuing traffic challans involves manual identification of licence plate numbers, verification of vehicle details against a database, and manual data entry. This manual process is time-consuming, error-prone, and can impose a significant burden on law enforcement officers. We can reduce the workload of the police by building an application which can automate the process using advanced computer vision techniques and integrating with the database. It involves the development of web and mobile applications that can capture the images of the licence plates and automatically extract the alphanumeric characters from the image using deep learning. Here we are utilising profound figuring out how to accomplish the number plate acknowledgment and character acknowledgment. Here we are involving YOLOv8 for identification. Unstructured data like images, audio, and text can be handled with ease by deep learning, making it an excellent choice for image recognition tasks. From raw data, deep learning algorithms can learn representations and automatically extract meaningful features. End-to-end learning is made possible by deep learning, which lets the model learn directly from the input data to the prediction for the output without using explicit feature engineering or other intermediate steps. In tasks like image classification and object detection, deep learning models can achieve high accuracy and deliver exceptional performance due to their capacity to capture intricate patterns and representations. We are likewise involving the high level PC vision methods for character reflection from the picture.
