Using spectral or spatial diversity associated with statistical processing has been proved useful to restore degraded texts in historical documents. By linear independent component analysis, we have been able to separate the main text from interfering patterns or hidden features in color or multispectral document images, and to cancel the showthrough-bleedthrough distortion from suitably registered graylevel recto-verso document images. By applying the same principles to RGB recto-verso images, we have now demonstrated that the recto and verso patterns can be separated as in the graylevel case, and their original colors can be reconstructed. Some examples from real documents will be shown.

Collaborative ranking of grid-enabled workflow service providers

Laforenza D;Nardini F M;Silvestri F
2008

Abstract

Using spectral or spatial diversity associated with statistical processing has been proved useful to restore degraded texts in historical documents. By linear independent component analysis, we have been able to separate the main text from interfering patterns or hidden features in color or multispectral document images, and to cancel the showthrough-bleedthrough distortion from suitably registered graylevel recto-verso document images. By applying the same principles to RGB recto-verso images, we have now demonstrated that the recto and verso patterns can be separated as in the graylevel case, and their original colors can be reconstructed. Some examples from real documents will be shown.
2008
Istituto di informatica e telematica - IIT
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
SLA
QoS
SOA
Grid
Information Retrieval
Vector Space Model
Collaborative Ranking
Service Discovery
Service Ranking
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/85993
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