ATM: Attentional Text Matting

Peng Kang, Jianping Zhang, Chen Ma, Guiling Sun; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 3902-3911

Image matting is a fundamental computer vision problem and has many applications. Previous image matting methods always focus on extracting a general object or portrait from the background in an image. In this paper, we try to solve the text matting problem, which extracts characters (usually WordArts) from the background in an image. Different from traditional image matting problems, text matting is much harder because of its foreground's three properties: smallness, multi-objectness, and complicated structures and boundaries. We propose a two-stage attentional text matting pipeline to solve the text matting problem. In the first stage, we utilize text detection methods to serve as the attention mechanism. In the second stage, we employ the attentional text regions and matting system to obtain mattes of these text regions. Finally, we post-process the mattes and obtain the final matte of the input image. We also construct a large-scale dataset with high-quality annotations consisting of 46,289 unique foregrounds to facilitate the learning and evaluation of text matting. Extensive experiments on this dataset and real images clearly demonstrate the superiority of our proposed pipeline over previous image matting methods on the task of text matting.