AUTHORS: Fabio Quattrini, R. Cucchiara, S. Cascianelli, V. Pippi
Work Package : All ITSERR WPs using Artificial Intelligence
Keywords: Digital Document Restoration, Virtual Unwrapping, Herculaneum Papyri, Ink Detection, Computer Vision, X-ray Micro-Computed Tomography, Artificial Intelligence, Volumetric Data, Fast Fourier Convolutions, Carbon-based Ink
Abstract
Recent advancements in Digital Document Restoration (DDR) have led to significant breakthroughs in analyzing highly damaged written artifacts. Among those, there has been an increasing interest in applying Artificial Intelligence techniques for virtually unwrapping and automatically detecting ink on the Herculaneum papyri collection. This collection consists of carbonized scrolls and fragments of documents, which have been digitized via X-ray tomography to allow the development of ad-hoc deep learningbased DDR solutions. In this work, we propose a modification of the Fast Fourier Convolution operator for volumetric data and apply it in a segmentation architecture for ink detection on the challenging Herculaneum papyri, demonstrating its suitability via deep experimental analysis. To encourage the research on this task and the application of the proposed operator to other tasks involving volumetric data, we will release our implementation (https://github.com/aimagelab/vffc).