Ink Analysis

Ink analysis is a rapidly evolving field employing advanced imaging techniques like hyperspectral imaging and X-ray computed tomography (CT) to characterize inks in documents, with primary objectives of authentication and text recovery from damaged artifacts. Current research focuses on developing and applying machine learning algorithms, including deep learning architectures like convolutional neural networks and clustering methods (e.g., k-means), to analyze spectral data and identify the number and types of inks present, even in challenging scenarios such as carbonized papyri. These advancements have significant implications for document forensics, historical preservation, and the recovery of lost texts, enabling more accurate analysis and authentication of documents and artifacts.