  License plate recognition systems have a very important role in many
applications such as toll management, parking control, and traffic management.
In this paper, a framework of deep convolutional neural networks is proposed
for Iranian license plate recognition. The first CNN is the YOLOv3 network that
detects the Iranian license plate in the input image while the second CNN is a
Faster R-CNN that recognizes and classifies the characters in the detected
license plate. A dataset of Iranian license plates consisting of
ill-conditioned images also developed in this paper. The YOLOv3 network
achieved 99.6% mAP, 98.26% recall, 98.08% accuracy, and average detection speed
is only 23ms. Also, the Faster R-CNN network trained and tested on the
developed dataset and achieved 98.97% recall, 99.9% precision, and 98.8%
accuracy. The proposed system can recognize the license plate in challenging
situations like unwanted data on the license plate. Comparing this system with
other Iranian license plate recognition systems shows that it is Faster, more
accurate and also this system can work in an open environment.
