STEPS: Scene Text Editing with Preserved Style Using Diffusion and Contrastive Style Encoding

arXiv:2609.38636 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Comments: 9 pages, 5 figures, 4 tables. A version of this paper appeared in PAKDD 2026 (LNCS vol. 16618)

Journal ref: Data Science: Foundations and Applications (PAKDD 2026), Lecture Notes in Computer Science, vol. 16618, pp. 473-484, Springer (2027)

DOI: 10.1007/978-981-92-1947-6_38

License: http://creativecommons.org/licenses/by/4.0/

The gist: We introduce Scene Text Editing with Preserved Style (STEPS), a novel diffusion model architecture for quality text replacement in images.

Terminology

Abstract

We introduce Scene Text Editing with Preserved Style (STEPS), a novel diffusion model architecture for quality text replacement in images. Scene Text Editing (STE), also known as Visual Text Editing, consists of changing the textual content in an image while conserving the original style, e.g. font, colors, orientation, background, etc. STEPS advances the state of the art in STE through directed focus on improved style preservation. We introduce a style encoder for visual text that captures style independently of textual content, and a model architecture that combines the style encoder with multiple semantic conditions (target text characters encoding and rendered glyphs). STEPS achieves superior results to previous STE methods in style preservation, output readability, and subjective quality.

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