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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15944
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dc.contributor.authorKonwer A.-
dc.contributor.authorBhunia A.K.-
dc.contributor.authorBhowmick A.-
dc.contributor.authorBhunia A.K.-
dc.contributor.authorBanerjee P.-
dc.contributor.authorPratim Roy, Partha-
dc.contributor.authorPal U.-
dc.date.accessioned2020-12-02T11:42:03Z-
dc.date.available2020-12-02T11:42:03Z-
dc.date.issued2018-
dc.identifier.citationProceedings of International Conference on Pattern Recognition, (2018), 1103- 1108-
dc.identifier.isbn9.78154E+12-
dc.identifier.issn10514651-
dc.identifier.urihttps://doi.org/10.1109/ICPR.2018.8546105-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/15944-
dc.description.abstractStaff line removal is a crucial pre-processing step in Optical Music Recognition. In this paper we propose a novel approach for staff line removal, based on Generative Adversarial Networks. We convert staff line images into patches and feed them into a U-Net, used as Generator. The Generator intends to produce staff-less images at the output. Then the Discriminator does binary classification and differentiates between the generated fake staff-less image and real ground truth staff less image. For training, we use a Loss function which is a weighted combination of L2 loss and Adversarial loss. L2 loss minimizes the difference between real and fake staff-less image. Adversarial loss helps to retrieve more high quality textures in generated images. Thus our architecture supports solutions which are closer to ground truth and it reflects in our results. For evaluation we consider the ICDAR/GREC 2013 staff removal database. Our method achieves superior performance in comparison to other conventional approaches on the same dataset. © 2018 IEEE.-
dc.description.sponsorshipInternational Association for Pattern Recognition (IAPR)-
dc.language.isoen_US-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.relation.ispartofProceedings of International Conference on Pattern Recognition-
dc.subjectAdversarial Loss-
dc.subjectGenerative Adversarial Network-
dc.subjectStaff-line Removal-
dc.subjectU-Net-
dc.titleStaff line Removal using Generative Adversarial Networks-
dc.typeConference Paper-
dc.scopusid57192375073-
dc.scopusid57188719920-
dc.scopusid57192375518-
dc.scopusid57203526133-
dc.scopusid57202820553-
dc.scopusid56880478500-
dc.scopusid57200742116-
dc.affiliationKonwer, A., Department of ECE, Institute of Engineering Management, Kolkata, India-
dc.affiliationBhunia, A.K., Department of ECE, Institute of Engineering Management, Kolkata, India-
dc.affiliationBhowmick, A., Department of ECE, Institute of Engineering Management, Kolkata, India-
dc.affiliationBhunia, A.K., Department of CSE, Indian Institute of Technology, Roorkee, India-
dc.affiliationBanerjee, P., Department of CSE, Institute of Engineering and Management, Kolkata, India-
dc.affiliationRoy, P.P., Department of CSE, Indian Institute of Technology Roorkee, India-
dc.affiliationPal, U., CVPR Unit, Indian Statistical Institute, Kolkata, India-
dc.identifier.conferencedetails24th International Conference on Pattern Recognition, ICPR 2018, 20-24 August 2018-
Appears in Collections:Conference Publications [CS]

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