Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

Ink Detection Using K-Mean Clustering in Hyperspectral Document

Version 1 : Received: 30 June 2020 / Approved: 1 July 2020 / Online: 1 July 2020 (09:01:50 CEST)

How to cite: Raza, R.; Saeed, S.; Sharif, R. Ink Detection Using K-Mean Clustering in Hyperspectral Document. Preprints 2020, 2020070002 (doi: 10.20944/preprints202007.0002.v1). Raza, R.; Saeed, S.; Sharif, R. Ink Detection Using K-Mean Clustering in Hyperspectral Document. Preprints 2020, 2020070002 (doi: 10.20944/preprints202007.0002.v1).

Abstract

In document forensic, Ink mismatch relays very important information about forgeries in this way we can find out the authenticity of documents. Finding out and distinguishing these unique inks from the multispectral document is very challenging task. In this paper we proposed the method to identify the inks using clustering. We used K-Mean clustering instead of widely known Fuzzy C-Means Clustering (FCM) and successfully identity the number of inks. For the purpose of optimizing and improving our results we used two optimization techniques such as Elbow and silhouette optimization techniques.

Subject Areas

Forensic; Ink Mismatch; Clustering; K-means algorithm; Elbow; Silhouette

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