VATr++: Choose Your Words Wisely for Handwritten Text Generation
This work addresses critical bottlenecks in HTG research for the community, offering incremental improvements in performance and establishing a foundation for fair comparisons.
The study tackled the problem of input impact and lack of standardized evaluation in Styled Handwritten Text Generation, proposing strategies for input preparation and training regularization that improved performance and generalization, and introduced a standardized evaluation protocol to benchmark existing approaches.
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.