RewriteNet: Reliable Scene Text Editing with Implicit Decomposition of Text Contents and Styles

Understand

Scene text editing (STE), which converts a text in a scene image into the desired text while preserving an original style, is a challenging task due to a complex intervention between text and style.

• In this paper, we propose a novel STE model, referred to as RewriteNet, that decomposes text images into content and style features and re-writes a text in the original image.
• Specifically, RewriteNet implicitly distinguishes the content from the style by introducing scene text recognition.
• Additionally, independent of the exact supervisions with synthetic examples, we propose a self-supervised training scheme for unlabeled real-world images, which bridges the domain gap between synthetic and real data.