This paper aims to develop the study of historical printed ornaments with modern unsupervised computer vision. We highlight three complex tasks that are of critical interest to book historians: clustering, element discovery, and unsupervised change localization. For each of these tasks, we introduce an evaluation benchmark, and we adapt and evaluate state-of-the-art models. Our Rey's Ornaments dataset is designed to be a representative example of a set of ornaments historians would be interested in. It focuses on an XVIIIth century bookseller, Marc-Michel Rey, providing a consistent set of ornaments with a wide diversity and representative challenges. Our results highlight the limitations of state-of-the-art models when faced with real data and show simple baselines such as k-means or congealing can outperform more sophisticated approaches on such data. Our dataset and code can be found at https://printed-ornaments.github.io/.
@article{arxiv.2408.08633,
title = {Historical Printed Ornaments: Dataset and Tasks},
author = {Sayan Kumar Chaki and Zeynep Sonat Baltaci and Elliot Vincent and Remi Emonet and Fabienne Vial-Bonacci and Christelle Bahier-Porte and Mathieu Aubry and Thierry Fournel},
journal= {arXiv preprint arXiv:2408.08633},
year = {2024}
}