Historical Printed Ornaments: Dataset and Tasks

• URL: http://arxiv.org/abs/2408.08633v1
• Date: Fri, 16 Aug 2024 09:54:12 GMT
• Title: Historical Printed Ornaments: Dataset and Tasks
• Authors: Sayan Kumar Chaki, Zeynep Sonat Baltaci, Elliot Vincent, Remi Emonet, Fabienne Vial-Bonacci, Christelle Bahier-Porte, Mathieu Aubry, Thierry Fournel,
• Abstract summary: 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. Our Rey's Ornaments dataset is designed to be a representative example of a set of ornaments historians would be interested in.
• Score: 12.309062405756686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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