Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized
  Herculaneum Papyri
Fabio Quattrini Vittorio Pippi Silvia Cascianelli Rita Cucchiara
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 learning-based 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)