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dc.creatorKhan, K. (Khalil)-
dc.creatorRoh, B. (Byeong-hee)-
dc.creatorAli, J. (Jehad)-
dc.creatorUllah-Khan, R. (Rehan)-
dc.creatorUddin, I. (Irfan)-
dc.creatorHassan, S. (Saqlain)-
dc.creatorRiaz, R. (Rabia)-
dc.creatorAhmad, N. (Nasir)-
dc.date.accessioned2023-10-16T12:37:16Z-
dc.date.available2023-10-16T12:37:16Z-
dc.date.issued2020-
dc.identifier.citationKhan, K. (Khalil); Roh, B. (Byeong-hee); Ali, J. (Jehad); et al. "PHND: Pashtu handwritten numerals database and deep learning benchmark". Plos One. 15 (9), 2020, e0238423es_ES
dc.identifier.issn1932-6203-
dc.identifier.urihttps://hdl.handle.net/10171/67659-
dc.description.abstractIn this paper we introduce a real Pashtu handwritten numerals dataset (PHND) having 50,000 scanned images and make publicly available for research and scientific use. Although more than fifty million people in the world use this language for written and oral communication, no significant efforts are devoted to the Pashtu Optical Character Recognition (POCR). We present a new approach for Pahstu handwritten numerals recognition (PHNR) based on deep neural networks. We train Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) on high-frequency numerals for feature extraction and classification. We evaluated the performance of the proposed algorithm on the newly introduced Pashtu handwritten numerals database PHND and Bangla language number database CMATERDB 3.1.1. We obtained best recognition rate of 98.00% and 98.64% on PHND and CMATERDB 3.1.1. respectively.es_ES
dc.description.sponsorshipThis research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01431) supervised by the IITP (Institute for Information & communications Technology Promotion.es_ES
dc.language.isoenges_ES
dc.publisherPloses_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectPashtu handwritten numerals dataset (PHND)es_ES
dc.subjectHandwritinges_ES
dc.subjectPashtu optical character recognition (POCR)es_ES
dc.subjectAlgorithmes_ES
dc.subjectDeep learninges_ES
dc.subjectRecognitiones_ES
dc.titlePHND: Pashtu handwritten numerals database and deep learning benchmarkes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.noteThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.es_ES
dc.identifier.doi10.1371/journal.pone.0238423-
dadun.citation.number9es_ES
dadun.citation.publicationNamePlos Onees_ES
dadun.citation.startingPagee0238423es_ES
dadun.citation.volume15es_ES
dc.identifier.pmid32877456-

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