Wedge Detection for Predictive Graphical Annotation of Cuneiform Tablets in 3D

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dc.contributor.authorBullenkamp, Jan Philippen_US
dc.contributor.authorMara, Huberten_US
dc.contributor.editorCorsini, Massimilianoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorKuijper, Arjanen_US
dc.contributor.editorKutlu, Hasanen_US
dc.date.accessioned2024-09-15T09:57:19Z
dc.date.available2024-09-15T09:57:19Z
dc.date.issued2024
dc.description.abstractAs Digital Ancient Near Studies (DANES) and Ancient Language Processing (ALP) require larger volumes of annotated documents, we focus on automatically computed graphical annotations for cuneiform tablets, which are among the oldest documents spanning at least three millennia of human history. Cuneiform consists of wedges imprinted on clay tablets, which are best captured by high-resolution 3D acquisition. Existing Open Access 3D models are therefore our data base and have been postprocessed by the GigaMesh software framework, which ensures clean meshes and provides MSII filter responses. This refers to the Gaussian curvature, which is a function value for each vertex of the mesh used for watersheds along the 2D manifold. Watershed labels are determined by non-minimum suppression using a merge parameter. The label contours, i.e., the polygonal lines enclosing the wedges, are smoothed using the Savitzky-Golay filter with the goal of projecting a low-poly line in 2D that can be quickly corrected by the image-based annotation tool known as Cuneur, which allows experts to annotate wedges, signs and semantics. Once the wedges have been determined, wedge types can be automatically proposed using the orientation of the wedges, which is the first step towards a paleographic representation of cuneiform signs, known as PaleoCode.en_US
dc.description.sectionheadersAI-based Wedge and Glyph Detection
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20241260
dc.identifier.isbn978-3-03868-248-6
dc.identifier.issn2312-6124
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/gch.20241260
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/gch20241260
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Computer graphics; Applied computing → Archaeology
dc.subjectComputing methodologies → Computer graphics
dc.subjectApplied computing → Archaeology
dc.titleWedge Detection for Predictive Graphical Annotation of Cuneiform Tablets in 3Den_US
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GCH 2024 - Eurographics Workshop on Graphics and Cultural Heritage