Improved license plate recognition system with deep learning methods and block-based approach

This study investigates the effectiveness of current deep learning techniques in license plate detection and makes important contributions. Instead of classifying the characters on Turkish license plates with a single classifier, the characters are divided into blocks of numbers and letters using various image processing techniques and a separate classifier is used for each block. This has been observed to improve character classification accuracy and hence license plate recognition accuracy. This approach eliminated the possibility of misclassification of similar letters and numbers and improved the character classification accuracy from 95.9% to 99.6%. In addition, a new character feature dataset was created and a deep learning model was trained with this dataset. Integrating this model into the system increased the classification accuracy to 99.7%. The YOLOv8 object detection model, trained using CUDA technology, achieved a mAP of 98.9%. The overall accuracy of the whole system in license plate recognition reached 97.3%. This study proves the effectiveness of current deep learning methods and the proposed block-based character recognition approach in license plate recognition.