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dc.contributor.authorKala, Ahmet
dc.contributor.authorGorgel, Pelin
dc.contributor.authorUcan, Birsen
dc.contributor.authorKilic, Niyazi
dc.contributor.authorUcan, Osman N.
dc.date.accessioned2021-03-02T23:21:27Z
dc.date.available2021-03-02T23:21:27Z
dc.date.issued2009
dc.identifier.citationGorgel P., Kilic N., Ucan B., Kala A., Ucan O. N. , "A BACKPROPAGATION NEURAL NETWORK APPROACH FOR OTTOMAN CHARACTER RECOGNITION", INTELLIGENT AUTOMATION AND SOFT COMPUTING, cilt.15, sa.3, ss.451-462, 2009
dc.identifier.issn1079-8587
dc.identifier.otherav_11f48001-0bc2-4bee-bcbb-f669634c6eef
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/17537
dc.identifier.urihttps://doi.org/10.1080/10798587.2009.10643041
dc.description.abstractThe Ottoman Empire established in 1299 and continued 6 centuries covering an area of about 5.6 million squared km. The Empire left a large collection of valuable archives interesting to historians from all over the world. Investigation and understanding these documents will shed light on the history of the world. In order to achieve access of the considered information by worldwide scientists, it is essential to translate Ottoman characters into Latin alphabet. Thus, we aimed to recognize the Ottoman characters using Artificial Neural Network (ANNT) and compared it with Support Vector Machine (SVM) approaches. We used printed type of Ottoman scripts in image acquisition. Pre-processing such as normalization and edge detection were implemented. Multilayer perceptions of ANN were trained using the backpropagation learning algorithm. As a result of our research, we are able to classify the Ottoman characters with 85.5% classification accuracy using the proposed recognition system.
dc.language.isoeng
dc.subjectAlgoritmalar
dc.subjectMühendislik ve Teknoloji
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectBilgisayar Bilimleri
dc.subjectKontrol ve Sistem Mühendisliği
dc.subjectBilgisayar Bilimi
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectOTOMASYON & KONTROL SİSTEMLERİ
dc.titleA BACKPROPAGATION NEURAL NETWORK APPROACH FOR OTTOMAN CHARACTER RECOGNITION
dc.typeMakale
dc.relation.journalINTELLIGENT AUTOMATION AND SOFT COMPUTING
dc.contributor.departmentİstanbul Üniversitesi , ,
dc.identifier.volume15
dc.identifier.issue3
dc.identifier.startpage451
dc.identifier.endpage462
dc.contributor.firstauthorID38142


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