  Recent efforts on scene text erasing have shown promising results. However,
existing methods require rich yet costly label annotations to obtain robust
models, which limits the use for practical applications. To this end, we study
an unsupervised scenario by proposing a novel Self-supervised Text Erasing
(STE) framework that jointly learns to synthesize training images with erasure
ground-truth and accurately erase texts in the real world. We first design a
style-aware image synthesis function to generate synthetic images with diverse
styled texts based on two synthetic mechanisms. To bridge the text style gap
between the synthetic and real-world data, a policy network is constructed to
control the synthetic mechanisms by picking style parameters with the guidance
of two specifically designed rewards. The synthetic training images with
erasure ground-truth are then fed to train a coarse-to-fine erasing network. To
produce better erasing outputs, a triplet erasure loss is designed to enforce
the refinement stage to recover background textures. Moreover, we provide a new
dataset (called PosterErase), which contains 60K high-resolution posters with
texts and is more challenging for the text erasing task. The proposed method
has been extensively evaluated with both PosterErase and the widely-used
SCUT-Enstext dataset. Notably, on PosterErase, our unsupervised method achieves
5.07 in terms of FID, with a relative performance of 20.9% over existing
supervised baselines.
