Preprint Concept Paper Version 1 This version is not peer-reviewed

Automatic Ink Mismatch Detection in Hyper Spectral Images Using K-means Clustering

Version 1 : Received: 27 June 2020 / Approved: 28 June 2020 / Online: 28 June 2020 (19:26:25 CEST)

How to cite: Raza Shah, N.; Talha, M.; Imtiaz, F.; Azmat, A. Automatic Ink Mismatch Detection in Hyper Spectral Images Using K-means Clustering. Preprints 2020, 2020060341 (doi: 10.20944/preprints202006.0341.v1). Raza Shah, N.; Talha, M.; Imtiaz, F.; Azmat, A. Automatic Ink Mismatch Detection in Hyper Spectral Images Using K-means Clustering. Preprints 2020, 2020060341 (doi: 10.20944/preprints202006.0341.v1).

Abstract

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

Subject Areas

Hyper spectral Document Images; Non-destructive Analysis; Forensics Document; Ink Mismatch Detection; K-means Clustering

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