Deep License Plate Recognition in IllConditioned Environments With IllConditional Data Augmentation In general, the conventional LPR systems consist of the following modules: feature extraction, license plate locating, character segmentation, and character recognition. The performances of these module are strongly correlated with some low level image features, e.g., edges, colors, and textures. These low level image features can be influenced significantly by the illumination and view angle variations such that the recognition accuracy is degraded. Recently, the deep learning technologies make the conventional visionbased recognition technologies getting significant improvement in terms of feature discrimination and recognition accuracy. In this paper, we aim to develop a novel deep learning based LPR system with the illconditional data augmentation. Therefore, this paper is expected to the following contributions. First, we apply the WebGL technology to augment the training database for the illconditioned outdoor environments. Second, we apply the YOLOv2 DNN architecture to develop deep license plate recognition system in the illconditioned environments with recognition accuracy 98%.

Image Generation Framework for Unbalanced License Plate Data Set Deep learning based methods have achieved promising performance in the image fields, but they are significantly sensitive to the distribution of data. To obtain balanced data, the acquisition of annotation data is very timeconsuming and laborious. Generative Adversarial Networks have made a lot of progress in data generation but may degrade greatly in the unbalanced data set. In this paper, we propose an image generation framework to generate photorealistic, various, and balanced images of text. Specifically, we add the module for training unpaired images (Umodule) and target selector to our framework, and the target selector uses text string to select images in the extended real images, which contain real images and the generated images by the Umodule, in addition, global generator and local enhancer network are applied to improve the quality of the generated images. We demonstrate our method on the Chinese license plates, and the unbalance of the license plate data set is shown in the provincial category and the special license plates. The Inception Score and the FID Score are used as metrics to validate our method. The experimental results show that the Inception Score of the generated images is close to that of the real images, and our method achieves the lower FID Score than the stateoftheart. In the SYSUITS dataset, the accuracy of license plate recognition has been largely improved, especially for the provinces with no or few real samples.