Computerized number plate recognition is one of the several facts systems that is utilized records extraction from the given automobile image and tend the records for further usage in safe, comfortable and modernistic transportation system. The uniqueness of this challenge is that whether the photograph is blurred or not, our machine can enhance the given photograph and use it on the device learning models in addition throughout the proposed project, the viewers will seen only once: YOLO V8 model Once the ROI is detected, it’ll in all probability be finer with preprocessing steps before its miles fed to the CNN model. The Dataset for different Indian wide variety Plates’ Font was created, which contain total 8,999 pictures of various alpho-numerical characters. Accuracy of ninety-one. Five percentage is received. The extracted and sorted characters of the wide variety plate is cross-checked with the Indian RTO database by Roboflow and the data concerning which RTO the enter vehicle photograph belong to, is provided by the company Uralytics. The experimental results demonstrate an impressive accuracy of 96.5%. The system successfully extracts and categorizes characters from the number plate, and these characters are cross-checked against the Indian Regional Transport Office (RTO) database. This research contributes to the advancement of computerized number plate recognition systems by addressing the challenge of blurred images, enhancing the overall accuracy and reliability of the system. The proposed methodology exhibits promise for applications in secure and modern transportation systems.