Restoration and content analysis of ancient manuscripts via color space based segmentation

Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one Jg. 18; H. 3; S. e0282142
Hauptverfasser: Hanif, Muhammad, Tonazzini, Anna, Hussain, Syed Fawad, Khalil, Akhtar, Habib, Usman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 22.03.2023
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Cluster Analysis
Clustering
Color
Computer Simulation
Content analysis
Data reduction
Degradation
Documents
Historic preservation
Image segmentation
Methods
Normal Distribution
Physical Sciences
Pixels
Probabilistic models
Protection and preservation
Registration
Research and Analysis Methods
Restoration
Science, Ancient
Segmentation
Spatial data
Texture
Pakistan
ISSN:1932-6203, 1932-6203
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0282142