R-CNN based polygonal wedge detection: learned from annotated 3D renderings and mapped photographs of ppen data Cuneiform tablets
Motivated by the demands of Digital Assyriology and the challenges of detecting cuneiform signs, we propose a new approach using R-CNN architecture to classify and localize wedges. We utilize the 3D models of 1977 cuneiform tablets from the Frau Professor Hilprecht Collection available as pen data....
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
2023
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| In: |
Eurographics Workshop on Graphics and Cultural Heritage 2023
Year: 2023, Pages: 47-56 |
| DOI: | 10.2312/gch.20231157 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.2312/gch.20231157
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| Verfasserangaben: | E. Stötzner, T. Homburg, J.P. Bullenkamp and H. Mara |
| Zusammenfassung: | Motivated by the demands of Digital Assyriology and the challenges of detecting cuneiform signs, we propose a new approach using R-CNN architecture to classify and localize wedges. We utilize the 3D models of 1977 cuneiform tablets from the Frau Professor Hilprecht Collection available as pen data. About 500 of these tablets have a transcription available in the Cuneiform Digital Library Initiative (CDLI) database. We annotated 21.000 cuneiform signs as well as 4.700 wedges resulting in the new open data Mainz Cuneiform Benchmark Dataset (MaiCuBeDa), including metadata, cropped signs, and partially wedges. The latter is also a good basis for manual paleography. Our inputs are MSII renderings computed using the GigaMesh Software Framework and photographs having the annotations automatically transferred from the renderings. |
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| Beschreibung: | Gesehen am 07.09.2023 |
| Beschreibung: | Online Resource |
| DOI: | 10.2312/gch.20231157 |