Scene text editing (STE) is the task of changing the textual content in an image while keeping the original style, such as font, color, and background. It has significant practical value in applications like translation, advertising, and augmented reality. However, training STE models usually depends on paired images: the same scene captured before and after the text is altered.
The new arXiv paper, "Real In, Real Out," questions this dependency. The authors ask whether STE can be accomplished using only real data, without paired examples. This would remove the need for synthetic datasets or labor-intensive annotation, which are common bottlenecks in the field.
The abstract highlights the practical motivation but does not yet include experimental results. As an announcement, the paper sets out a research direction rather than a proven solution, leaving open the question of how well a real-data-only approach can preserve style while editing text.