A Survey of Modern Handwriting Generation Models

Author(s)	Ms. Thrishaa J, Ms. Agamya David, Ms. Neha Venkatesh, Dr. Kiran Y.C
Country	India
Abstract	This survey reviews the evolution of handwriting generation models, covering GAN, Transformer, VAE, and diffusion-based approaches. It highlights how these methods improve style representation, personalization, and data efficiency. The paper also examines the Emuru architecture and its implementation in InkPersona, which enables one-shot handwriting generation from a single sample. Key challenges and future research directions for personalized and real-time handwriting synthesis are discussed.
Keywords	Personalized handwriting, handwriting synthesis, few-shot learning, zero-shot learning, Emuru, variational autoencoder, transformer, diffusion models
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-12
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9815
Short DOI	https://doi.org/hbf832

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International Journal on Science and Technology