LIST: low illumination scene text detector with automatic feature enhancement

Low illumination, under which discriminative clues are buried in the captured images, is an under-investigated but noteworthy issue in wild scene text detection. Existing deep learning approaches suffer from the scarcity of training data and illumination-sensitive feature representation. To address these issues, we propose a Low Illumination Scene Text (LIST) Detector training with authentic synthetic data and integrating dedicated feature enhancement modules. Specifically, we adopt a lightweight and non-reference low-light scene text image synthesis network to acquire adequate training data through pixel-wisely adjusting the dynamic range curve of normal-light images. Moreover, illumination invariant feature representation is learned through dual path feature extraction stem with intensity adjusted inputs, and feature fusion branch with automatically designed fusion cell. In the end, the enhanced feature is fed into a segmentation-based layer to localize arbitrary shape text instances. We construct a labeled real-world scene text image dataset called "DarkText" and conduct extensive experiments to validate the advantages of our proposed framework over state-of-the-art competitors.