GARN: A Novel Generative Adversarial Recognition Network for EndtoEnd Scene Character Recognition Deep neural networks have shown their powerful ability in scene character recognition tasks; however, in real life applications, it is often hard to find a large amount of highquality scene character images for training these networks. In this paper, we proposed a novel endtoend network named Generative Adversarial Recognition Networks (GARN) for accurate natural scene character recognition in an endtoend way. The proposed GARN consists of a generation part and a classification part. For the generation part, the purpose is to produce diverse realistic samples to help the classifier overcome the overfitting problem. While in the classification part, a multinomial classifier is trained along with the generator in the form of a game to achieve better character recognition performance. That is, the proposed GARN has the ability to augment scene character data by its generation part and recognize scene characters by its classification part. It is trained in an adversarial way to improve recognition performance. The experimental results on benchmark datasets and the comparisons with the stateoftheart methods show the effectiveness of the proposed GARN in scene character recognition.

Scene Text Recognition with AutoAligned Feature Generator Scene text recognition has attracted increasing attention in computer vision due to its various applications. Most of the existing scene text recognition methods are under the encoderdecoder framework. In order to improve text feature learning of these methods, Generative Adversarial Networks (GANs) are recently integrated to generate clean text images without distorted letters. However, the existing GANs assume the input images are spatially aligned, while the words in natural images are often in irregular shapes. The misalignment brings a big problem for both image generation and text recognition. In this paper, we present a novel text feature alignment network to solve this problem. Our method can handle both horizontal and vertical images with irregular texts. Our proposed framework is endtoend trainable, and extensive experiments on several public benchmarks demonstrate its superiority in terms of both effectiveness and efficiency.