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Recent advancements in Digital Document Restoration (DDR) have led tosignificant breakthroughs in analyzing highly damaged written artifacts. Amongthose, there has been an increasing interest in applying ArtificialIntelligence techniques for virtually unwrapping and automatically detectingink on the Herculaneum papyri collection. This collection consists ofcarbonized scrolls and fragments of documents, which have been digitized viaX-ray tomography to allow the development of ad-hoc deep learning-based DDRsolutions. In this work, we propose a modification of the Fast FourierConvolution operator for volumetric data and apply it in a segmentationarchitecture for ink detection on the challenging Herculaneum papyri,demonstrating its suitability via deep experimental analysis. To encourage theresearch on this task and the application of the proposed operator to othertasks involving volumetric data, we will release our implementation(https://github.com/aimagelab/vffc)

Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri