FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework

Alloy Das, Sanket Biswas, Prasun Roy, Subhankar Ghosh, Umapada Pal, Michael Blumenstein, Josep Lladós, Saumik Bhattacharya; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1944-1954

Scene Text Editing (STE) is a challenging research problem that primarily aims towards modifying existing texts in an image while preserving the background and the font style of the original text. Despite its utility in numerous real-world applications existing style-transfer-based approaches have shown sub-par editing performance due to (1) complex image backgrounds (2) diverse font attributes and (3) varying word lengths within the text. To address such limitations in this paper we propose a novel font-agnostic scene text editing and rendering framework named FASTER for simultaneously generating text in arbitrary styles and locations while preserving a natural and realistic appearance and structure. A combined fusion of target mask generation and style transfer units with a cascaded self-attention mechanism has been proposed to focus on multi-level text region edits to handle varying word lengths. Extensive evaluation on a real-world database with further subjective human evaluation study indicates the superiority of FASTER in both scene text editing and rendering tasks in terms of model performance and efficiency. The code and pre-trained models have been released in our Github repo.