Stroke-Based Scene Text Erasing Using Synthetic Data

• URL: http://arxiv.org/abs/2104.11493v1
• Date: Fri, 23 Apr 2021 09:29:41 GMT
• Title: Stroke-Based Scene Text Erasing Using Synthetic Data
• Authors: Zhengmi Tang, Tomo Miyazaki, Yoshihiro Sugaya, and Shinichiro Omachi
• Abstract summary: Scene text erasing can replace text regions with reasonable content in natural images. The lack of a large-scale real-world scene-text removal dataset allows the existing methods to not work in full strength. We enhance and make full use of the synthetic text and consequently train our model only on the dataset generated by the improved synthetic text engine. This model can partially erase text instances in a scene image with a bounding box provided or work with an existing scene text detector for automatic scene text erasing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene text erasing, which replaces text regions with reasonable content in natural images, has drawn attention in the computer vision community in recent years. There are two potential subtasks in scene text erasing: text detection and image inpainting. Either sub-task requires considerable data to achieve better performance; however, the lack of a large-scale real-world scene-text removal dataset allows the existing methods to not work in full strength. To avoid the limitation of the lack of pairwise real-world data, we enhance and make full use of the synthetic text and consequently train our model only on the dataset generated by the improved synthetic text engine. Our proposed network contains a stroke mask prediction module and background inpainting module that can extract the text stroke as a relatively small hole from the text image patch to maintain more background content for better inpainting results. This model can partially erase text instances in a scene image with a bounding box provided or work with an existing scene text detector for automatic scene text erasing. The experimental results of qualitative evaluation and quantitative evaluation on the SCUT-Syn, ICDAR2013, and SCUT-EnsText datasets demonstrate that our method significantly outperforms existing state-of-the-art methods even when trained on real-world data.

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