Kurdish Sign Language Recognition System

Abstract

Deaf people all around the world face difficulty to communicate with the others. Hence, they use their own language to communicate with each other. On the other hand, it is difficult for deaf people to get used to technological services such as websites, television, mobile applications, and so on. This project aims to design a prototype system for deaf people to help them to communicate with other people and computers without relying on human interpreters. The proposed system is for letter-based Kurdish Sign Language (KuSL) which has not been introduced before. The system would be a real-time system that takes actions immediately after detecting hand gestures. Three algorithms for detecting KuSL have been implemented and tested, two of them are well-known methods that have been implemented and tested by other researchers, and the third one has been introduced in this paper for the 1st time. The new algorithm is named Gridbased gesture descriptor. It turned out to be the best method for the recognition of Kurdish hand signs. Furthermore, the result of the algorithm was 67% accuracy of detecting hand gestures. Finally, the other well-known algorithms are named scale invariant feature transform and speeded-up robust features, and they responded with 42% of accuracy.

Effective Kurdish Sign Language Detection and Classification Using Convolutional Neural Networks

“…4 compares the proposed CNN model's performance to various classi cation machine-learning approaches for sign language recognition. It is important to note that the SIFT, SURF, and GRIDDING methods [31] have the lowest rate of recognition accuracy among all models, which are 42%, 42%, and 67%, respectively. Moreover, the ResNet (CNN) model [25] is located in the lower position in terms of classes and has 5 classes with a 78.49% accuracy rate, followed by the ANN model [32] with 10 classes and 98% accuracy.…”

“…Recently, Several studies have been performed on several sign languages, including ASL [13][14] [15][16] [17] [18] [19][20], BSL [21]. Arabic sign language (ArSL) [1][22] [23], Turkish sign language [24][25],Persian sign language [26] [27] [28], Indian sign language [29] [30], and to the best of the authors' knowledge, the only three available researches have been on KuSL, including 12 classes [31], 10 classes [32], and 84 classes [33].…”

Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network

“…Studies in the literature mainly used a limited number of classes (Table I). For KuSL, and to our best knowledge, the only two available studies have used 12 (Hashim and Alizadeh, 2018) and 10 signs (Mahmood et al, 2018). In this study, and to increase the validity of the proposed ASLR for KuSL, we have designed a dataset that contains 84 signs including digits, alphabets and words.…”

“…Finally, and regarding the ASLR applied to KuSL, two studies have been conducted using image processing tools. First, Hashim and Alizadeh, 2018, developed an algorithm using a grid-based gesture descriptor on the hand gesture image for 12 Kurdish letters, produced following image enhancement and segmentation steps. The achieved accuracy of the proposed model was 67%.…”

“…ASLR is a field that has been studied for various sign languages (Almasre and Al-Nuaim, 2016, Maass et al, 2002, Mahmood et al, 2018. However, few research studies have been conducted on the KuSL because it is new (Abdul et al, 2020, Hashim and Alizadeh, 2018, Mahmood et al, 2018.…”