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Supporting the analysis of a large coin hoard with AI-based methodsuse asterix (*) to get italics
Chrisowalandis Deligio, Karsten Tolle, David Wigg-WolfPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p>In the project "Classifications and Representations for Networks: From types and characteristics to linked open data for Celtic coinages" (ClaReNet) we had access to image data for one of the largest Celtic coin hoards ever found: Le Câtillon II with nearly 70,000 coins. Our aim was not to develop new processes, but rather to demonstrate how existing tools can be used to support the numismatic task of processing and analysing large complexes of coins, thus validating the enormous potential of IT-based methods. The main steps involved are the pre-sorting of coins by size (denomination), the attribution of individual coins to classes or types, and finally the identification of which coins were struck by individual dies.&nbsp;<br>The process from digitisation of a hoard as images to an actual die study is lengthy and work-intensive. In testing methods to support each of the steps, we focussed &nbsp;particularly on methods that do not need any prior knowledge of the material, in order &nbsp;to explore whether these methods can be applied to a dataset for which there is no more information than the images themselves. The different steps were evaluated against information provided by the numismatist working on the hoard (class and die attributions of the coins), who was also involved in different stages of the process.&nbsp;</p> <p><br>The result is a workflow that can be used in future work on large coin finds, thus supporting numismatists and significantly speeding up the work of identification and analysis.&nbsp;</p> <p><br>This paper also presents tools, visualisation methods and extensions that proved useful, both for the individual processes, as well as for communicating with the numismatists and integrating their expertise. Earlier phases of our work were presented at CAA 2022 in Oxford.</p> should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https:// should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https:// should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
machine learning, celtic coins, classification, unsupervised learning, object detection
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Computational archaeology
No need for them to be recommenders of PCIArchaeology. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
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2023-08-30 15:31:16
Alex Brandsen