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.

研究了手写体文本生成的影响因素，提出了输入准备和训练规范化策略，以获得更好的性能和更好的泛化能力，并提出了手写体文本生成的评估协议的标准化和全面的对比分析，推动该领域的进展。

VATr++：为手写文本生成明智选择您的字词

VATr++: Choose Your Words Wisely for Handwritten Text Generation

Styled Handwritten Text Generation (Styled HTG) is an important task in
document analysis, aiming to generate text images with the handwriting of given
reference images. In recent years, there has been significant progress in the
development of deep learning models for tackling this task. Being able to
measure the performance of HTG models via a meaningful and representative
criterion is key for fostering the development of this research topic. However,
despite the current adoption of scores for natural image generation evaluation,
assessing the quality of generated handwriting remains challenging. In light of
this, we devise the Handwriting Distance (HWD), tailored for HTG evaluation. In
particular, it works in the feature space of a network specifically trained to
extract handwriting style features from the variable-lenght input images and
exploits a perceptual distance to compare the subtle geometric features of
handwriting. Through extensive experimental evaluation on different word-level
and line-level datasets of handwritten text images, we demonstrate the
suitability of the proposed HWD as a score for Styled HTG. The pretrained model
used as backbone will be released to ease the adoption of the score, aiming to
provide a valuable tool for evaluating HTG models and thus contributing to
advancing this important research area.

通过深度学习模型，我们提出了一种适用于装饰手写文本生成（Styled HTG）评估的手写距离（HWD）方法，它能够从可变长度的输入图像中提取手写风格特征，并利用感知距离来比较手写的微妙几何特征，经过大量实验对手写文本图像的不同数据集进行评估，表明我们提出的 HWD 方法适用于 Styled HTG 的评分，对于评估 HTG 模型提供了有价值的工具，从而推动了这一重要研究领域的发展。