Handwriting generation is an important direction in computer vision and natural language processing.Traditional models such as AE, VAE, and GAN have gradually developed, while diffusion models have attracted much attention due to their better generation quality and stability. At present, advanced methods include GANwriting, which extracts writing styles through style encoders and generates texts with matching styles and accurate content; VATr++ combines visual perception modules with Transformer architecture, and uses multi-level conditional control and hybrid attention mechanisms to achieve accurate imitation of complex handwriting styles; WordStylist combines semantic information and style features based on the latent diffusion model (LDM) to generate texts with accurate styles; DiffusionPen generates handwritten images by denoising in the latent space through content encoding and style encoding to ensure consistency of content and style; DiffCJK combines conditional diffusion models and language-specific knowledge to achieve high-quality generation with excellent local details and global structures based on the characteristics of Chinese, Japanese, and Korean characters. Although LDM performs well in generation quality and multi-language support, it still faces challenges such as insufficient efficiency, poor adaptability to few samples, and limited style diversity.
In this work, I want to generate Styled handwritten images based on LDM at the word level to further improve the generation efficiency, enhance the style generalization ability, and achieve more refined style control.
The implementation will be done in Python / Pytorch.
The thesis consists of the following milestones:
– Explore techniques to accelerate the diffusion process (e.g., fast sampling algorithms or segmented denoising strategies) to reduce generation time.
– Optimize the representation of the latent space to further reduce the computational complexity.
– Try using different mechanisms or enhanced learning methods to improve adaptability to extreme styles
– Further experiments and optimizations on the learning process and network architecture.
– Evaluate performance and compare with other new technologies