Enhancement of Multispectral Images of Degraded Documents by Employing Spatial Information

Hollaus, Fabian; Gau, Melanie; Sablatnig, Robert

doi:10.1109/icdar.2013.36

Cited by 17 publications

(7 citation statements)

References 7 publications

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“…HSI analysis for refurbishment of ancient documents was used by R.J. Hejdam et al [17]., while targeting the practical scenarios where ink could not be detected or differentiated via naked human eye. Image enhancement for degraded images was proposed by F. Hollaus et al [6]. In which he proposed that degraded images can be enhanced via utilizing spectral as well as spatial information.…”

Section: Related Workmentioning

confidence: 99%

Ink Detection Using K-Mean Clustering in Hyperspectral Document

Raza¹,

Saeed²,

Sharif³

2020

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%

Ink Detection Using K-Mean Clustering in Hyperspectral Document

Raza¹,

Saeed²,

Sharif³

2020

Preprint

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“…Hedjam et al [11] also proposed a mathematical model for improving the readability of extremely deteriorated text. Hollaus et al [12] introduced a method for enhancing degraded and ancient writings captured by multispectral imaging system on the basis of spectral as well as spatial information. Brauns et al [6] designed a hyperspectral imaging technique based on Fourier transform for the nondestructive analysis of potentially duplicitous documents.…”

Section: Related Workmentioning

confidence: 99%

“…R. J. Hejdam et al [2] used HSI analysis for restoration of old reports, and focused on the practical scenarios where ink might not be identified by naked eyes. F. Hollaus et al [3] proposed picture improvement for debased pictures by utilizing the spectral as well as spatial data. A. Abbas et al [4] proposed a novel unsupervised clustering method.…”

Section: Introductionmentioning

confidence: 99%

Forgery Detection in a Questioned Hyperspectral Document Image using K-means Clustering

Yaseen¹,

Aftab²,

mahrukh³

2020

Preprint

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Hyperspectral imaging allows for analysis of images in several hundred of spectral bands depending on the spectral resolution of the imaging sensor. Hyperspectral document image is the one which has been captured by a hyperspectral camera so that the document can be observed in the different bands on the basis of their unique spectral signatures. To detect the forgery in a document various Ink mismatch detection techniques based on hyperspectral imaging have presented vast potential in differentiating visually similar inks. Inks of different materials exhibit different spectral signature even if they have the same color. Hyperspectral analysis of document images allows identification and discrimination of visually similar inks. Based on this analysis forensic experts can identify the authenticity of the document. In this paper an extensive ink mismatch detection technique is presented which uses KMean Clustering to identify different inks on the basis of their unique spectral response and separates them into different clusters.

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“…Hyperspectral imaging has recently emerged as an efficient nondestructive tool for detection and identification of forensic traces such as bloodstain analysis and latent print analysis [4]. It is also useful to questioned document examination for ink discrimination, document age estimation and restoration of historical documents [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%