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。

HWD：一种新的风格手写文本生成评估得分