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

Noman Raza Shah; Muhammad Talha; Fizza Imtiaz; Aneeqah Azmat

doi:10.20944/preprints202006.0341.v1

Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

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

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.

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

Engineering, Electrical and Electronic Engineering

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

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