Ink classification in historical documents using hyperspectral imaging and machine learning methods.

Ink identification using only spectral reflectance information poses significant challenges due to material degradation, aging, and spectral overlap between ink classes. This study explores the use of hyperspectral imaging and machine learning techniques to classify three distinct types of inks: pure metallo-gallate, carbon-containing, and non-carbon-containing inks. Six supervised classification models, including five traditional algorithms (Support Vector Machines, K-Nearest Neighbors, Linear Discriminant Analysis, Random Forest, and Partial Least Squares Discriminant Analysis) and one Deep Learning-based model, were evaluated.