  Diffusion models have gained attention for image editing yielding impressive
results in text-to-image tasks. On the downside, one might notice that
generated images of stable diffusion models suffer from deteriorated details.
This pitfall impacts image editing tasks that require information preservation
e.g., scene text editing. As a desired result, the model must show the
capability to replace the text on the source image to the target text while
preserving the details e.g., color, font size, and background. To leverage the
potential of diffusion models, in this work, we introduce Diffusion-BasEd Scene
Text manipulation Network so-called DBEST. Specifically, we design two
adaptation strategies, namely one-shot style adaptation and text-recognition
guidance. In experiments, we thoroughly assess and compare our proposed method
against state-of-the-arts on various scene text datasets, then provide
extensive ablation studies for each granularity to analyze our performance
gain. Also, we demonstrate the effectiveness of our proposed method to
synthesize scene text indicated by competitive Optical Character Recognition
(OCR) accuracy. Our method achieves 94.15% and 98.12% on COCO-text and
ICDAR2013 datasets for character-level evaluation.
