Ink classification in historical documents using hyperspectral imaging and machine learning methods

Abstract

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. The methodology integrates data fusion from different imaging systems, sample extraction, ground truth creation, and a post-processing step to increase uniformity. The evaluation was performed using both mock-up samples and historical documents, achieving micro-averaged accuracy above 90% for all models. The best performance was obtained using the DL-based model (98% F1-score), followed by the Support Vector Machine model. In the case study documents, the overall performance of the traditional model was better. This study highlights the potential of hyperspectral imaging combined with machine learning for non-invasive ink identification and mapping, even under challenging conditions, contributing to the conservation and analysis of historical manuscripts.