Comparison of Ink Classification Capabilities of Classic Hyperspectral Similarity Features

Abstract

Ink classification is an active topic in historic and forensic document analysis. In this work, we compared the ink classification capabilities of five commonly used and wellproven similarity measures for classification of hyperspectral imaging (HSI). They are Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), Euclidean Distance (ED), Spectral Information Divergence (SID) and Binary Encoding (BE). These techniques were well explored in different fields of HSI; however, they are not investigated in the field of ink classification. This study reveals the ink classification capabilities of these similarity measures. A combination of different types and colors of inks from different manufactures were used to create sample text. The SAM obtained higher accuracy compared to other methods and also identified that, inks that have nearly similar spectral signatures, cause a decline in accuracies due to misclassification between similar classes.