Analysis demonstrates ink discrimination using PCA and K-Means clustering in hyperspectral imaging, indicating effective separation of ink categories.
Abstract This paper presents an analysis of a hyperspectral image (HSI) of a document with the goal of detecting different inks. The approach includes an analysis of the spectral bands, visual and spectral analysis of the text lines, dimensionality reduction using Principal Component Analysis (PCA), and unsupervised clustering using K-Means to estimate ink categories. The methodology is validated through a comparison of clustering before and after PCA. Visual outputs and spectral plots are provided to display observed trends and ink separation results.
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Maryam Batool (2025) studied this question.
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