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)

翻译：数字文档修复（DDR）领域的最新进展，使分析严重受损的文字文物取得了突破性进展。其中，应用人工智能技术对赫库兰尼姆纸莎草卷进行虚拟展开及自动墨迹检测，日益受到关注。该收藏包含碳化的卷轴和文献残片，通过X射线断层扫描进行数字化，从而推动了基于深度学习的专用DDR解决方案的开发。本文提出了一种适用于体积数据的快速傅里叶卷积算子的改进版本，并将其应用于分割架构中，用于对具有挑战性的赫库兰尼姆纸莎草进行墨迹检测，通过深入的实验分析证明了其适用性。为促进该任务的研究以及将所提出的算子应用于涉及体积数据的其他任务，我们将公开我们的实现代码（https://github.com/aimagelab/vffc）。