  Recognizing car license plates in natural scene images is an important yet
still challenging task in realistic applications. Many existing approaches
perform well for license plates collected under constrained conditions, eg,
shooting in frontal and horizontal view-angles and under good lighting
conditions. However, their performance drops significantly in an unconstrained
environment that features rotation, distortion, occlusion, blurring, shading or
extreme dark or bright conditions. In this work, we propose a robust framework
for license plate recognition in the wild. It is composed of a tailored
CycleGAN model for license plate image generation and an elaborate designed
image-to-sequence network for plate recognition. On one hand, the CycleGAN
based plate generation engine alleviates the exhausting human annotation work.
Massive amount of training data can be obtained with a more balanced character
distribution and various shooting conditions, which helps to boost the
recognition accuracy to a large extent. On the other hand, the 2D attentional
based license plate recognizer with an Xception-based CNN encoder is capable of
recognizing license plates with different patterns under various scenarios
accurately and robustly. Without using any heuristics rule or post-processing,
our method achieves the state-of-the-art performance on four public datasets,
which demonstrates the generality and robustness of our framework. Moreover, we
released a new license plate dataset, named "CLPD", with 1200 images from all
31 provinces in mainland China. The dataset can be available from:
https://github.com/wangpengnorman/CLPD_dataset.
