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

TL;DR

This paper introduces a modified Fast Fourier Convolution operator tailored for volumetric data, applied to detect ink on carbonized Herculaneum papyri, advancing digital restoration techniques with promising experimental results.

Contribution

It proposes a novel volumetric Fast Fourier Convolution operator and demonstrates its effectiveness in ink detection on challenging archaeological data.

Findings

Effective ink detection on Herculaneum papyri

Improved segmentation accuracy with the proposed operator

Open-source implementation available for further research

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 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)