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.

Orientation Robust Scene Text Recognition in Natural Scene In recent years, scene text recognition has achieved significant improvement and various stateoftheart recognition approaches have been proposed. This paper focused on recognizing text in natural photos of equipment nameplates, which has wide applications in industrial automations. This task only receives little attentions in previous works. The challenge of this problem comes from multiorientation, curved, noisy and blurry text patches in equipment nameplates. To address this problem, we propose a deep model for text recognition in multioriented nameplates, namely, Orientation Robust Scene Text Recognition (ORSTR). Specifically, our model employs a rectification module to transform curved, distorted or multiorientation text to nearhorizontal text with a carefully designed rectification module. Once the nearhorizontal text has been generated, recognition network will output the predictions of text patches. Our scene text recognition model achieves 90 . 8% recognition accuracy on equipment nameplate dataset which outperforms previous scene text recognition model (CRNN) about 0 . 8%. Several extensive experiments have been conducted to verify the effectiveness of our model.