DeepEraser: Deep Iterative Context Mining for Generic Text Eraser

• URL: http://arxiv.org/abs/2402.19108v1
• Date: Thu, 29 Feb 2024 12:39:04 GMT
• Title: DeepEraser: Deep Iterative Context Mining for Generic Text Eraser
• Authors: Hao Feng, Wendi Wang, Shaokai Liu, Jiajun Deng, Wengang Zhou, Houqiang Li
• Abstract summary: DeepEraser is a recurrent architecture that erases the text in an image via iterative operations. DeepEraser is notably compact with only 1.4M parameters and trained in an end-to-end manner.
• Score: 103.39279154750172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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