  In this work, we present DeepEraser, an effective deep network for generic
text removal. DeepEraser utilizes a recurrent architecture that erases the text
in an image via iterative operations. Our idea comes from the process of
erasing pencil script, where the text area designated for removal is subject to
continuous monitoring and the text is attenuated progressively, ensuring a
thorough and clean erasure. Technically, at each iteration, an innovative
erasing module is deployed, which not only explicitly aggregates the previous
erasing progress but also mines additional semantic context to erase the target
text. Through iterative refinements, the text regions are progressively
replaced with more appropriate content and finally converge to a relatively
accurate status. Furthermore, a custom mask generation strategy is introduced
to improve the capability of DeepEraser for adaptive text removal, as opposed
to indiscriminately removing all the text in an image. Our DeepEraser is
notably compact with only 1.4M parameters and trained in an end-to-end manner.
To verify its effectiveness, extensive experiments are conducted on several
prevalent benchmarks, including SCUT-Syn, SCUT-EnsText, and Oxford Synthetic
text dataset. The quantitative and qualitative results demonstrate the
effectiveness of our DeepEraser over the state-of-the-art methods, as well as
its strong generalization ability in custom mask text removal. The codes and
pre-trained models are available at https://github.com/fh2019ustc/DeepEraser
