  Styled Handwritten Text Generation (HTG) has received significant attention
in recent years, propelled by the success of learning-based solutions employing
GANs, Transformers, and, preliminarily, Diffusion Models. Despite this surge in
interest, there remains a critical yet understudied aspect - the impact of the
input, both visual and textual, on the HTG model training and its subsequent
influence on performance. This study delves deeper into a cutting-edge
Styled-HTG approach, proposing strategies for input preparation and training
regularization that allow the model to achieve better performance and
generalize better. These aspects are validated through extensive analysis on
several different settings and datasets. Moreover, in this work, we go beyond
performance optimization and address a significant hurdle in HTG research - the
lack of a standardized evaluation protocol. In particular, we propose a
standardization of the evaluation protocol for HTG and conduct a comprehensive
benchmarking of existing approaches. By doing so, we aim to establish a
foundation for fair and meaningful comparisons between HTG strategies,
fostering progress in the field.
