Quantitative Ink Analysis: Estimating the Number of Inks in Documents through Hyperspectral Imaging
This work addresses ink analysis for document forensics to detect forgery, but it is incremental as it applies existing clustering methods to hyperspectral data.
The paper tackled the problem of distinguishing visually similar inks in document forensics by proposing a hyperspectral imaging technique to estimate the number of distinct inks, with results showing k-means clustering achieving superior classification performance on a dataset with 12 lines indicating multiple inks.
In the field of document forensics, ink analysis plays a crucial role in determining the authenticity of legal and historic documents and detecting forgery. Visual examination alone is insufficient for distinguishing visually similar inks, necessitating the use of advanced scientific techniques. This paper proposes an ink analysis technique based on hyperspectral imaging, which enables the examination of documents in hundreds of narrowly spaced spectral bands, revealing hidden details. The main objective of this study is to identify the number of distinct inks used in a document. Three clustering algorithms, namely k-means, Agglomerative, and c-means, are employed to estimate the number of inks present. The methodology involves data extraction, ink pixel segmentation, and ink number determination. The results demonstrate the effectiveness of the proposed technique in identifying ink clusters and distinguishing between different inks. The analysis of a hyperspectral cube dataset reveals variations in spectral reflectance across different bands and distinct spectral responses among the 12 lines, indicating the presence of multiple inks. The clustering algorithms successfully identify ink clusters, with k-means clustering showing superior classification performance. These findings contribute to the development of reliable methodologies for ink analysis using hyperspectral imaging, enhancing the