Handwritten Text Generation

Handwritten text generation (HTG) aims to create realistic images of handwritten text, focusing on replicating both the content and stylistic nuances of a given writer. Current research heavily utilizes diffusion models, often enhanced with techniques to improve style control from limited samples (e.g., one-shot generation) and address challenges like generating rare characters or adapting styles across languages. This field is significant for advancing document image analysis, providing tools for data augmentation to improve handwriting recognition accuracy, and offering new avenues for generating synthetic datasets where real data is scarce or expensive to obtain.