Swapping text in scene images while preserving original fonts, colors, sizes
and background textures is a challenging task due to the complex interplay
between different factors. In this work, we present SwapText, a three-stage
framework to transfer texts across scene images. First, a novel text swapping
network is proposed to replace text labels only in the foreground image.
Second, a background completion network is learned to reconstruct background
images. Finally, the generated foreground image and background image are used
to generate the word image by the fusion network. Using the proposing
framework, we can manipulate the texts of the input images even with severe
geometric distortion. Qualitative and quantitative results are presented on
several scene text datasets, including regular and irregular text datasets. We
conducted extensive experiments to prove the usefulness of our method such as
image based text translation, text image synthesis, etc.

提出了 SwapText 框架，它是通过三个阶段来实现场景图像中文本交换的，包括用于替换前景图像标签的新型文本交换网络、学习用于重建背景图像的背景完成网络和使用生成的前景图像和背景图像来生成单词图像的融合网络。在几个场景文本数据集上展示了定性和定量结果，并进行了广泛的实验以证明我们的方法的有用性，如基于图像的文本翻译、文本图像合成等。