The goal of this research is to employ OCR and conventional image processing methods to create a reliable system for detecting and recognizing vehicle number plates. The system's goal is to improve vehicle identification and traffic monitoring in metropolitan settings. It uses a number of image preprocessing techniques, including contrast correction, noise reduction, and grayscale conversion, to enhance the quality of number plate photos that are collected. The method precisely isolates individual characters by implementing character segmentation using bounding box extraction and morphological procedures after preprocessing. The OCR model is trained using a manually selected dataset that includes pictures of license plates in various settings. While data cleaning procedures handle problems like missing or ambiguous characters, the dataset is painstakingly tagged to guarantee accuracy in character identification. Character dimensions and contextual information are taken into consideration during feature selection and engineering, which improves model performance even further. In order to precisely identify and recreate the vehicle number plate, the segmented characters are first used to extract attributes, which are subsequently matched to a collection of known characters. For convenience and connection with traffic control systems, the finished product is saved as a text file. By offering a scalable solution for effective vehicle monitoring, this study not only shows that conventional techniques for number plate recognition are feasible, but it also advances intelligent transportation systems.