Historical Printed Ornaments: Dataset and Tasks
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arXiv
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| Format: | Preprint |
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2024
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| author | Chaki, Sayan Kumar Baltaci, Zeynep Sonat Vincent, Elliot Emonet, Remi Vial-Bonacci, Fabienne Bahier-Porte, Christelle Aubry, Mathieu Fournel, Thierry |
| author_facet | Chaki, Sayan Kumar Baltaci, Zeynep Sonat Vincent, Elliot Emonet, Remi Vial-Bonacci, Fabienne Bahier-Porte, Christelle Aubry, Mathieu Fournel, Thierry |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Historical Printed Ornaments: Dataset and Tasks Chaki, Sayan Kumar Baltaci, Zeynep Sonat Vincent, Elliot Emonet, Remi Vial-Bonacci, Fabienne Bahier-Porte, Christelle Aubry, Mathieu Fournel, Thierry Computer Vision and Pattern Recognition 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/. |
| title | Historical Printed Ornaments: Dataset and Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.08633 |