Nonlinear model and constrained ML for removing back-to-front interferences from recto-verso documents

Authors:
Francesca Martinelli;Emanuele Salerno;Ivan Gerace;Anna Tonazzini
Affiliations:
National Research Council of Italy, CNR, Institute of Information Science and Technologies, Via G.Moruzzi, 1 - 56124 Pisa, Italy;National Research Council of Italy, CNR, Institute of Information Science and Technologies, Via G.Moruzzi, 1 - 56124 Pisa, Italy;University of Perugia, Department of Mathematics and Informatics, Via Vanvitelli, 1 - 06123 Perugia, Italy and National Research Council of Italy, CNR, Institute of Information Science and Technol ...;National Research Council of Italy, CNR, Institute of Information Science and Technologies, Via G.Moruzzi, 1 - 56124 Pisa, Italy
Venue:
Pattern Recognition
Year:
2012

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Abstract

In this paper, we approach the removal of back-to-front interferences from scans of double-sided documents as a blind source separation problem, and extend our previous linear mixing model to a more effective nonlinear mixing model. We consider the front and back ideal images as two individual patterns overlapped in the observed recto and verso scans, and apply an unsupervised constrained maximum likelihood technique to separate them. Through several real examples, we show that the results obtained by this approach are much better than the ones obtained through data decorrelation or independent component analysis. As compared to approaches based on segmentation/classification, which often aim at cleaning a foreground text by removing all the textured background, one of the advantages of our method is that cleaning does not alter genuine features of the document, such as color or other structures it may contain. This is particularly interesting when the document has a historical importance, since its readability can be improved while maintaining the original appearance.