  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.
