Semantic-Driven Deep Learning Network for Visual Communication in Compound Chinese Character Design

Synthesizing compound Chinese characters is pivotal for modern typography and interaction design, yet intricate stroke coupling often leads to structural collapse in existing models. Current methods, which rely on loose implicit mappings, frequently produce disordered layouts and blurred artifacts. To rectify this, the author proposes latent structure-guided diffusion, which explicitly injects geometric priors into the diffusion process. By coordinating a latent cross-condition projector for semantic alignment and a latent deformable layout field for differentiable stroke manipulation, latent structure-guided diffusion ensures precise compositional control. Experiments confirmed its superior performance, noting that it achieved a structural similarity index of 0.95 and a mean edge gradient of 0.94, thus significantly outperforming state-of-the-art baselines in structural fidelity and stylistic coherence.