  Hyper-spectral imaging has become the latest trend in the field of optical
imaging systems. Among various other applications, hyper-spectral imaging has
been widely used for analysis of printed and handwritten documents. This paper
proposes an efficient technique for estimating the number of different but
visibly similar inks present in a Hyper spectral Document Image. Our approach
is based on un-supervised learning and does not require any prior knowledge of
the dataset. The algorithm was tested on the iVision HHID dataset and has
achieved comparable results with the state of the algorithms present in the
literature. This work can prove to be effective when employed during the early
stages of forgery detection in Hyper-spectral Document Images.
