From pixels to strokes: A survey on generative handwriting

This survey presents a comprehensive overview of recent advances in Handwritten Text Generation (HTG), focusing on prominent model architectures such as Generative Adversarial Networks (GANs), Transformer-based models, and Diffusion Models. We analyze their respective strengths and limitations in terms of training stability, style transfer, and output fidelity. In addition, we discuss a range of modeling strategies — from sequence modeling to style-content disentanglement. We also expose persistent challenges related to model training, evaluation metrics, and dataset scarcity. By critically discussing these methodologies, we aim to provide information on state-of-the-art practices and emerging trends, offering a resource for researchers and practitioners seeking to advance the field of handwriting generation.