IMPROVED TURKISH SIGN LANGUAGE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS / TOLGA YIRTICI; SUPERVISOR: ASST. PROF. DR. KAMİL YURTKAN

Yazar: Katkıda bulunan(lar):Dil: İngilizce 2022Tanım: 114 sheets; 31 cm. Includes CDİçerik türü:
  • text
Ortam türü:
  • unmediated
Taşıyıcı türü:
  • volume
Konu(lar): Tez notu: Thesis (PHD) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department Özet: ABSTRACT This thesis started with idea of creating a robust system for Turkish Sign Language recognition. Fingerspelling of Turkish Sign Language alphabet is chosen for these purposes. Turkish Sign Language alphabet consists of 29 letters just like in speaking language. Alphabet letters can be used to form a word. Two different systems designed in this manner, one with a region-based object detection method and other is an information content-based feature selection. The designed systems are employed with AlexNet architecture using transfer learning. AlexNet is a pre-trained Convolutional Neural Network that utilized for classification problems. The novel object detection method is tested with three different algorithms and achieved the best result of 0.997 mean Average Precision and 0.9982 accuracy rate. The information content-based feature selection method with employed the same AlexNet architecture, used a novel feature selection algorithm and achieved more than 80% accuracy rate. Both of the systems are trained and tested on the dataset created for this study in a studio.
Materyal türü: Thesis
Mevcut
Materyal türü Geçerli Kütüphane Koleksiyon Yer Numarası Durum Notlar İade tarihi Barkod Materyal Ayırtmaları
Thesis Thesis CIU LIBRARY Tez Koleksiyonu Tez Koleksiyonu D 304 Y47 2022 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Computer Engineering Department T2682
Suppl. CD Suppl. CD CIU LIBRARY Görsel İşitsel D 304 Y47 2022 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Computer Engineering Department CDT2682
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Thesis (PHD) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department

Includes bibliography (sheets 106-114)

ABSTRACT
This thesis started with idea of creating a robust system for Turkish Sign Language
recognition. Fingerspelling of Turkish Sign Language alphabet is chosen for these
purposes. Turkish Sign Language alphabet consists of 29 letters just like in speaking
language. Alphabet letters can be used to form a word. Two different systems designed
in this manner, one with a region-based object detection method and other is an
information content-based feature selection. The designed systems are employed with
AlexNet architecture using transfer learning. AlexNet is a pre-trained Convolutional
Neural Network that utilized for classification problems. The novel object detection
method is tested with three different algorithms and achieved the best result of 0.997
mean Average Precision and 0.9982 accuracy rate. The information content-based
feature selection method with employed the same AlexNet architecture, used a novel
feature selection algorithm and achieved more than 80% accuracy rate. Both of the
systems are trained and tested on the dataset created for this study in a studio.

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