A comparison of Arabic sign language dynamic gesture recognition models

Almasre, Miada A.; Al-Nuaim, Hana

doi:10.1016/j.heliyon.2020.e03554

Cited by 28 publications

(10 citation statements)

References 22 publications

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“…The system performance was tested, which indicated that the highest accuracy rate was 99 %. Almasre and Al-Nuaim [17] proposed a Kinect-based prototype model for recognition of specific Arabic Sign Language hand gesture words. This model obtained 83 % offline classification accuracy using SVM, RF and KNN algorithms.…”

Section: Related Studiesmentioning

confidence: 99%

Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features

Karacı

Akyol

Turut

2021

Applied Computer Systems

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In this study, a machine learning-based system, which recognises the Turkish sign language person-independent in real-time, was developed. A leap motion sensor was used to obtain raw data from individuals. Then, handcraft features were extracted by using Euclidean distance on the raw data. Handcraft features include finger-to-finger, finger -to-palm, finger -to-wrist bone, palm-to-palm and wrist-to-wrist distances. LR, k-NN, RF, DNN, ANN single classifiers were trained using the handcraft features. Cascade voting approach was applied with two-step voting. The first voting was applied for each classifier’s final prediction. Then, the second voting, which voted the prediction of all classifiers at the final decision stage, was applied to improve the performance of the proposed system. The proposed system was tested in real-time by an individual whose hand data were not involved in the training dataset. According to the results, the proposed system presents 100 % value of accuracy in the classification of one hand letters. Besides, the recognition accuracy ratio of the system is 100 % on the two hands letters, except “J” and “H” letters. The recognition accuracy rates were 80 % and 90 %, respectively for “J” and “H” letters. Overall, the cascade voting approach presented a high average classification performance with 98.97 % value of accuracy. The proposed system enables Turkish sign language recognition with high accuracy rates in real time.

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Section: Related Studiesmentioning

confidence: 99%

Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features

Karacı

Akyol

Turut

2021

Applied Computer Systems

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“…Almasre et al [17] proposed a dynamic prototype model (DPM) that recognizes some ARSL gestured dynamic words using Kinect as a sensor. The DPM used eleven predictive models based on three algorithms' various parameter settings (SVM, RF, and KNN).…”

Section: Related Workmentioning

confidence: 99%

Approach for Improving User Interface Based on Gesture Recognition

et al. 2021

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Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.

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“…Arabic is the 4th most spoken language in the world. Arabic Sign Language (ArSL) was a certified main language for talking and listening impaired in Arab nations [1]. Although Arabic is one of the global key languages, ArSL was still in its initial levels [2].…”

Section: Introductionmentioning

confidence: 99%

Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

Al-onazi¹,

Nour²,

Alshahran³

et al. 2023

Computers, Materials &Amp; Continua

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Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities. Several models have been available in the literature for sign language detection and classification for enhanced outcomes. But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures. The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network (DenseNet169) model. For gesture recognition and classification, a multilayer perceptron (MLP) classifier is exploited to recognize and classify the existence of sign language gestures. Lastly, the DHO algorithm is utilized for parameter optimization of the MLP model. The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects. The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced 3414 CMC, 2023, vol.75, no.2 gesture classification results than other techniques with maximum accuracy of 92.88%.

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A comparison of Arabic sign language dynamic gesture recognition models

Cited by 28 publications

References 22 publications

Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features

Real-Time Turkish Sign Language Recognition Using Cascade Voting Approach with Handcrafted Features

Approach for Improving User Interface Based on Gesture Recognition

Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

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