Robust Scene Text Detection via Learnable Scene Transformations

Scene text detection based on deep neural networks has been extensively studied in the last few years. However, the task of detecting texts in complex scenes such as bad weather and image distortions has not received sufficient attentions in existing works, which is crucial for real-world applications such as text translation, autonomous driving, etc. In this paper, we propose a novel strategy to automatically search for the effective scene transformation polices to augment images in the training phase. In addition, we build a new dataset, Robust-Text, to evaluate the robustness of text detection methods in real complex scenes. Experiments conducted on the ICDAR2015, MSRA-TD500 and Robust-Text datasets demonstrate that our method can effectively improve the robustness of text detectors in complex scenes.