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According to statistics, over half a billion vehicles are moving on the roads worldwide. All vehicles have a license plate as their primary identifier, which is one of the most suitable tools for vehicle identification. Automatic License Plate Recognition (ALPR) can be effective in improving road security, reducing traffic congestion, enhancing transportation efficiency, preventing car theft, and more. Traditional methods proposed for license plate detection mainly relied on manual feature extraction and lacked could not be generalized to variable image components in different conditions. With recent advancements in deep learning, algorithms have emerged that can automatically extract high-level representations of images in addition to learning complex image structures. Therefore, in this paper, the high capacity of deep neural networks is utilized for learning license plate identifiers. The proposed model consists of two stages: license plate localization and plate identification. For localization, a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) is used in an encoder-decoder network. The proposed model is evaluated on two datasets, FZU Cars and Stanford Cars, and based on the experimental results, it outperforms baseline methods in terms of accuracy on both datasets. [ABSTRACT FROM AUTHOR]