  In this paper, we are interested in editing text in natural images, which
aims to replace or modify a word in the source image with another one while
maintaining its realistic look. This task is challenging, as the styles of both
background and text need to be preserved so that the edited image is visually
indistinguishable from the source image. Specifically, we propose an end-to-end
trainable style retention network (SRNet) that consists of three modules: text
conversion module, background inpainting module and fusion module. The text
conversion module changes the text content of the source image into the target
text while keeping the original text style. The background inpainting module
erases the original text, and fills the text region with appropriate texture.
The fusion module combines the information from the two former modules, and
generates the edited text images. To our knowledge, this work is the first
attempt to edit text in natural images at the word level. Both visual effects
and quantitative results on synthetic and real-world dataset (ICDAR 2013) fully
confirm the importance and necessity of modular decomposition. We also conduct
extensive experiments to validate the usefulness of our method in various
real-world applications such as text image synthesis, augmented reality (AR)
translation, information hiding, etc.
