Classifiers and Image Processıng to Identify Sign Language Phonemes

Authors : Ebru Efeoğlu, Ayşe Tuna

Pages : 219-232

Doi:10.34186/klujes.1546178

View : 59 | Download : 71

Publication Date : 2024-12-31

Article Type : Research Paper

Abstract :One of the most visible symptoms of autism spectrum disorder is difficulty in speech and language. Difficulties in speech and language are generally very different for each child with autism spectrum disorder. Although some children with autism spectrum disorder can speak fluently, others will not be able to speak normally or will be even nonverbal. In all cases, parents try to communicate with, and understand their children’s needs, desires, and emotions. If a child with autism spectrum disorder cannot speak out loud, it is harder to communicate with him/her but there are other non-vocal methods for communication. In this paper, the benefits of teaching American sign language to children with autism spectrum disorder, the difficulties that families and children will experience while doing this, and technological solutions to these difficulties are presented. In parallel with advancements in technology, novel solutions to understand and use sign language have been proposed and these solutions are supposed to help parents who cannot understand sign language. Such solutions typically rely on image processing methods and classification algorithms to recognise sign language. Therefore, in this paper, the performance of various classification algorithms used to classify American Sign Language phonemes is compared. As the results show, when combined with image processing methods, classification algorithms can be used in various technological solutions aiming at helping to identify sign language phonemes.
Keywords : Otizm spektrum bozukluğu, sınıflandırıcılar, Görüntü işleme, Makine öğrenmesi, İşaret dili