Multiple Proposals for Continuous Arabic Sign Language Recognition

Hassan, Mohamed S.; Assaleh, Khaled; Shanableh, Tamer

doi:10.1007/s11220-019-0225-3

Cited by 40 publications

(27 citation statements)

References 39 publications

(42 reference statements)

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“…The problem of data availability for the Arabic sign language for public use, comparing to other known languages, such as English and Chinese [10]- [12], is an important aspect that challenges the process of Arabic sign language recognition and optimization. Scarcity and lack of quality and quantity terms of available datasets are of constant challenges [13]. This can be referred to as the problem of signer dependency and how each person expresses these signs [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of Arabic alphabets sign language using deep learning

Duwairi

Halloush

2022

IJECE

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<span>Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know.</span>

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Section: Introductionmentioning

confidence: 99%

Automatic recognition of Arabic alphabets sign language using deep learning

Duwairi

Halloush

2022

IJECE

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“…Whereas, body posture, mouth gestures, and facial expressions are the non-manual components of signs. Most of the signs are conveyed through manual components [4].The components of sign language can be seen in FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

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Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

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“…Hassan et al [16] produced three data sets of Arabian SL. They also used different techniques of recognition.…”

Section: Introductionmentioning

confidence: 99%

Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Sanaullah¹,

Kashif²,

Ahmad³

et al. 2022

Computers, Materials &Amp; Continua

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Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players. A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately. Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration. The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment. Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people. This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words. Finally, these are converted into the respective words/sentences within a reasonable time. This provide a real time solution for the deaf people to communicate and interact with the society.

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Multiple Proposals for Continuous Arabic Sign Language Recognition

Cited by 40 publications

References 39 publications

Automatic recognition of Arabic alphabets sign language using deep learning

Automatic recognition of Arabic alphabets sign language using deep learning

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

Sign Language to Sentence Formation: A Real Time Solution for Deaf People

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