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Hyperspectral Document Image Analysis using Unsupervised Learning
  • Muhammad Adeel Ajmal Khan ,
  • Ghanwa Haider ,
  • Atif Khan
Muhammad Adeel Ajmal Khan
Institute of Space and Technology

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Ghanwa Haider
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Atif Khan
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Abstract

Hyperspectral images are known to contain an extensive range of bands that offer a wealth of information that tri-spectral images cannot match. With an increased number of bands, hyperspectral images provide an enhanced level of reflectance and absorption values, making it possible to obtain more precise details at each point in the image. The HSI field continues to evolve and has already proven useful in various applications such as age prediction, handwritten OCR, word segmentation, ecological and hydrological science, forensics, and much more. This study involves showing the total number of bands of the hyperspectral image along with the starting and ending wavelength, displaying the 1st, 30th, 60th and last band of the HSI image, plotting the spectral responses of foreground pixels and applying k-means clustering to determine the number of inks present in the hyperspectral image document. Additionally, color-labeling techniques were utilized to identify the various colors of ink in the document.