![]() ![]() ![]() This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Data augmentation techniques are applied to increase the number of training and testing samples. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). ![]()
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