Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri
This work addresses the challenge of digital document restoration for highly damaged artifacts, specifically for researchers in archaeology and digital humanities, but is incremental as it modifies an existing method for a specific domain.
The authors tackled the problem of detecting ink on carbonized Herculaneum papyri by proposing a modified Fast Fourier Convolution operator for volumetric data, achieving suitability demonstrated through deep experimental analysis.
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)