# Towards Automatic License Plate Recognition in Challenging Conditions
## 2 Related Work
For performance evaluation, a few datasets that include images captured from various scenes under different conditions are tested. However, this method does not consider the complexity of the developed method. In [38], researchers use computer graphic scripts and GANs to generate and augment a large number of annotated, synthesized LPs with realistic colors, fonts, and character composition from a small number of real, manually labeled license plate images. In this work, generated and augmented data are mixed and used as training data for the LP recognition network modified from the DenseNet. Simulations reveal that the model trained from the generated mixed training data has much better generalization ability and achieves encouraging detection and recognition accuracy on multiple datasets, even with a very limited number of original real license plates. In [39], a new license plate recognition technique is developed in the wild. This method comprises a tailored CycleGAN model for license plate image generation and an elaborately designed image-to-sequence network for plate recognition. The CycleGAN-based plate generation engine eases the exhausting human annotation work. In this work, huge amounts of training data are obtained with a more balanced character distribution and various shooting conditions that boost the recognition accuracy to a large extent. Moreover, a 2D attentional-based license plate recognizer with an Xception-based CNN encoder is developed that is capable of recognizing various LPs with different patterns under various scenarios accurately. In [40], a new license plate dataset, to which the authors refer as the CCPD, is developed and tested under different circumstances, for instance, tilt, blur, rotate, or varying weather conditions. This work is novel in the sense that it provides a single platform for researchers to investigate the LP's prevailing issues. In [41], a novel end-to-end method for LP recognition without initial character segmentation is presented as LPRNet. Particularly, this method is inspired by recent breakthroughs in the DNNs and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on NVIDIA(r) GeForce(tm) GTX 1080 and 1.3 ms/plate on the Intel R Core(tm) i7-6700K CPU. The LPRNet consists of the lightweight CNN and can be trained end-to-end. The authors of this work recommend that the LPRNet algorithm may be used to create embedded solutions for LPR that feature high levels of accuracy even on challenging Chinese license plates. In [42], a multi-object rectified attention network (MORAN) is proposed for text recognition. The MORAN consists of a multi-object rectification network and an attention-based sequence recognition network. The multi-object rectification network is designed to rectify images that contain irregular text. It decreases the difficulty of recognition and enables the attention-based sequence recognition network to read irregular text. The attention-based sequence recognition network focuses on target characters and sequentially outputs the predictions. Further, to improve the sensitivity of the attention-based sequence recognition network, a fractional pickup algorithm is also developed for an attention-based decoder during the training phase. In [43], a novel decoupled attention network (DAN) is developed that decouples the alignment operation from using historical decoding results. The DAN is an effective, flexible, reliable, and robust end-to-end text recognizer and consists of three components: a feature encoder, a convolutional alignment module, and a decoupled text decoder that generates final predictions by jointly using the feature map and attention maps. [PERSON] et al. [44] used a wavelet transform at first to get the horizontal and vertical details of an image. Meanwhile, empirical mode decomposition (EMD) analysis was employed to deal with the projection data and locate the desired wave crest that indicates the position of a license plate appearing in any corner of the input image.