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dc.contributor.authorBin Ahmed, Saad
dc.contributor.authorHameed, Ibrahim A.
dc.contributor.authorNaz, Saeeda
dc.contributor.authorImran Razzak, Muhammad
dc.contributor.authorYusof, Rubiyah
dc.date.accessioned2019-12-12T08:25:32Z
dc.date.available2019-12-12T08:25:32Z
dc.date.created2019-10-15T07:16:56Z
dc.date.issued2019
dc.identifier.citationIEEE Access. 2019, .nb_NO
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/11250/2632867
dc.description.abstractThe similar nature of patterns may enhance the learning if the experience they attained during training is utilized to achieve maximum accuracy. This paper presents a novel way to exploit the transfer learning experience of similar patterns on handwritten Urdu text analysis. The MNIST pre-trained network is employed by transferring it’s learning experience on Urdu Nastaliq Handwritten Dataset (UNHD) samples. The convolutional neural network is used for feature extraction. The experiments were performed using deep multidimensional long short term (MDLSTM) memory networks. The obtained result shows immaculate performance on number of experiments distinguished on the basis of handwritten complexity. The result of demonstrated experiments show that pre-trained network outperforms on subsequent target networks which enable them to focus on a particular feature learning. The conducted experiments presented astonishingly good accuracy on UNHD dataset.nb_NO
dc.language.isoengnb_NO
dc.publisherIEEEnb_NO
dc.relation.urihttps://ieeexplore.ieee.org/document/8863484
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleEvaluation of Handwritten Urdu Text by Integration of MNIST Dataset Learning Experiencenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber13nb_NO
dc.source.journalIEEE Accessnb_NO
dc.identifier.doi10.1109/ACCESS.2019.2946313
dc.identifier.cristin1737032
dc.description.localcode© 2019. This is the authors' accepted and refereed manuscript to the article. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ The final authenticated version is available online at: http://dx.doi.org/10.1109/ACCESS.2019.2946313nb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal