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Historical Printed Ornaments: Dataset and Tasks

arXiv 2408.08633 ↗First posted 16 Aug 2024cs.CVcited by 0

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Abstract (arXiv)

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/.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 88 references that are in the map. First posted on arXiv 16 Aug 2024.

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References · 40

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