Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

摘要

Scene text recognition is an important task in computer vision. Despite tremendous progress achieved in the past few years, issues such as varying font styles, arbitrary shapes and complex backgrounds etc. have made the problem very challenging. In this work, we propose to improve text recognition from a new perspective by separating the text content from complex backgrounds, thus making the recognition considerably easier and significantly improving recognition accuracy. To this end, we exploit the generative adversarial networks (GANs) for removing backgrounds while retaining the text content . As vanilla GANs are not sufficiently robust to generate sequence-like characters in natural images, we propose an adversarial learning framework for the generation and recognition of multiple characters in an image. The proposed framework consists of an attention-based recognizer and a generative adversarial architecture. Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training. Benefiting from the character-level adversarial training, our framework requires only unpaired simple data for style supervision. Each target style sample containing only one randomly chosen character can be simply synthesized online during the training. This is significant as the training does not require costly paired samples or character-level annotations. Thus, only the input images and corresponding text labels are needed. In addition to the style normalization of the backgrounds, we refine character patterns to ease the recognition task. A feedback mechanism is proposed to bridge the gap between the discriminator and the recognizer. Therefore, the discriminator can guide the generator according to the confusion of the recognizer, so that the generated patterns are clearer for recognition. Experiments on various benchmarks, including both regular and irregular text, demonstrate that our method significantly reduces the difficulty of recognition. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.