  Although most current license plate (LP) recognition applications have been
significantly advanced, they are still limited to ideal environments where
training data are carefully annotated with constrained scenes. In this paper,
we propose a novel license plate recognition method to handle unconstrained
real world traffic scenes. To overcome these difficulties, we use adversarial
super-resolution (SR), and one-stage character segmentation and recognition.
Combined with a deep convolutional network based on VGG-net, our method
provides simple but reasonable training procedure. Moreover, we introduce
GIST-LP, a challenging LP dataset where image samples are effectively collected
from unconstrained surveillance scenes. Experimental results on AOLP and
GIST-LP dataset illustrate that our method, without any scene-specific
adaptation, outperforms current LP recognition approaches in accuracy and
provides visual enhancement in our SR results that are easier to understand
than original data.
