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. Preprints2020, 2020070002. https://doi.org/10.20944/preprints202007.0002.v1
Raza, R.; Saeed, S.; Sharif, R. Ink Detection Using K-Mean Clustering in Hyperspectral Document. Preprints 2020, 2020070002. https://doi.org/10.20944/preprints202007.0002.v1
Raza, R.; Saeed, S.; Sharif, R. Ink Detection Using K-Mean Clustering in Hyperspectral Document. Preprints2020, 2020070002. https://doi.org/10.20944/preprints202007.0002.v1
APA Style
Raza, R., Saeed, S., & Sharif, R. (2020). Ink Detection Using K-Mean Clustering in Hyperspectral Document. Preprints. https://doi.org/10.20944/preprints202007.0002.v1
Chicago/Turabian Style
Raza, R., Saifullah Saeed and Rizwan Sharif. 2020 "Ink Detection Using K-Mean Clustering in Hyperspectral Document" Preprints. https://doi.org/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.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
