  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
