TY  - BOOK
AU  - Yırtıcı,Tolga
AU  - Yurtkan,Kamil
TI  - IMPROVED TURKISH SIGN LANGUAGE RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS
PY  - 2022///
KW  - Object-oriented methods (Computer science)
KW  - Dissertations, Academic
KW  - Transfer learning (Machine learning)
N1  - Thesis (PHD) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department; Includes bibliography (sheets 106-114)
N2  - 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
ER  -