Uncertainty-awared generative network for Chinese scene text editing

A Chinese scene text editing (CSTE) method, based on research but incorporating uncertainty modeling and identifying effective technical solution, is proposed in this work． This new method optimizes prediction noise through an uncertainty-guided adjustment mechanism, improving the accuracy of noise estimation, thereby enhancing the clarity and structural integrity of the generated text. Additionally, by filtering irrelevant information from both textual and visual features, the method improves cross-modal alignment capabilities, achieving a seamless fusion of text and background textures．