"Our approach consists of a pipeline with two components: a sign detector and a wedge detector. The sign detector uses a RepPoints model with a ResNet18 backbone to locate individual cuneiform characters in the tablet segment image. The signs are then cropped based on the sign locations and fed into the wedge detector. The wedge detector is based on the idea of Point RCNN approach. It uses a Feature Pyramid Network (FPN) and RoI Align to predict the positions and classes of the wedges. The method is evaluated using different hyperparameters, and post-processing techniques such as Non-Maximum Suppression (NMS) are applied for refinement."

Stötzner E., Homburg T., Bullenkamp J.P. & Mara H. R-CNN based Polygonal Wedge Detection Learned from Annotated 3D Renderings and Mapped Photographs of Open Data Cuneiform Tablets. GCH 2023 - Eurographics Workshop on Graphics and Cultural Heritage. doi: http://dx.doi.org/10.2312/gch.20231157 #Research #Science #AI #ArtificialIntelligence #Archaeology #Cuneiform #ComputerScience #Modeling @science @archaeodons