  Text segmentation tasks have a very wide range of application values, such as
image editing, style transfer, watermark removal, etc.However, existing public
datasets are of poor quality of pixel-level labels that have been shown to be
notoriously costly to acquire, both in terms of money and time. At the same
time, when pretraining is performed on synthetic datasets, the data
distribution of the synthetic datasets is far from the data distribution in the
real scene. These all pose a huge challenge to the current pixel-level text
segmentation algorithms.To alleviate the above problems, we propose a
self-supervised scene text segmentation algorithm with layered decoupling of
representations derived from the object-centric manner to segment images into
texts and background. In our method, we propose two novel designs which include
Region Query Module and Representation Consistency Constraints adapting to the
unique properties of text as complements to Auto Encoder, which improves the
network's sensitivity to texts.For this unique design, we treat the
polygon-level masks predicted by the text localization model as extra input
information, and neither utilize any pixel-level mask annotations for training
stage nor pretrain on synthetic datasets.Extensive experiments show the
effectiveness of the method proposed. On several public scene text datasets,
our method outperforms the state-of-the-art unsupervised segmentation
algorithms.
