Learning to Remove Reflections for Text Images

Text images taken behind a piece of glass in the wild are largely contaminated by reflections. Directly applying existing reflection removal methods on text images with reflections cannot recover clear and correct text contents due to the ignorance of special characteristics of texts. This paper proposes a stacked framework to solve the text image reflection removal problem by specifically considering the regional properties of reflection and embedding the specific text priors into the estimation process in a unified manner. Experiment results on a newly collected dataset demonstrate that the proposed method outperforms state-of-the-art methods in recovering visually pleasant reflection-free images and recognizable text features.