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Generative Adversarial Networks (GAN) have attracted much research attentionrecently, leading to impressive results for natural image generation. However,to date little success was observed in using GAN generated images for improvingclassification tasks. Here we attempt to explore, in the context of car licenseplate recognition, whether it is possible to generate synthetic training datausing GAN to improve recognition accuracy. With a carefully-designed pipeline,we show that the answer is affirmative. First, a large-scale image set isgenerated using the generator of GAN, without manual annotation. Then, theseimages are fed to a deep convolutional neural network (DCNN) followed by abidirectional recurrent neural network (BRNN) with long short-term memory(LSTM), which performs the feature learning and sequence labelling. Finally,the pre-trained model is fine-tuned on real images. Our experimental results ona few data sets demonstrate the effectiveness of using GAN images: animprovement of 7.5% over a strong baseline with moderate-sized real data beingavailable. We show that the proposed framework achieves competitive recognitionaccuracy on challenging test datasets. We also leverage the depthwise separateconvolution to construct a lightweight convolutional RNN, which is about halfsize and 2x faster on CPU. Combining this framework and the proposed pipeline,we make progress in performing accurate recognition on mobile and embeddeddevices.

Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition