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Styled Handwritten Text Generation (Styled HTG) is an important task indocument analysis, aiming to generate text images with the handwriting of givenreference images. In recent years, there has been significant progress in thedevelopment of deep learning models for tackling this task. Being able tomeasure the performance of HTG models via a meaningful and representativecriterion 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 ofthis, we devise the Handwriting Distance (HWD), tailored for HTG evaluation. Inparticular, it works in the feature space of a network specifically trained toextract handwriting style features from the variable-lenght input images andexploits a perceptual distance to compare the subtle geometric features ofhandwriting. Through extensive experimental evaluation on different word-leveland line-level datasets of handwritten text images, we demonstrate thesuitability of the proposed HWD as a score for Styled HTG. The pretrained modelused as backbone will be released to ease the adoption of the score, aiming toprovide a valuable tool for evaluating HTG models and thus contributing toadvancing this important research area.

HWD: A Novel Evaluation Score for Styled Handwritten Text Generation