Arabic Sign Language Characters Recognition Based on Deep Learning Approach and a Simple Linear Classifier

Hasasneh, Ahmad

doi:10.5455/jjcit.71-1587943974

Cited by 10 publications

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“…A. Hasasneh [36] proposes the ArSL technique. The recognition and classification of Arabic alphabetical letters can be accomplished by unsupervised deep learning by employing a deep belief network (DBN) together with tiny images.…”

Section: Resnet152mentioning

confidence: 99%

Untitled

2024

INDJCSE

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Deaf individuals use sign language to interact with each other and with people in general. In most cases, an interpreter is needed in order for a hearing person to have a conversation with a deaf person. The study of recognizing visual signs is becoming more prevalent in the field of computer vision. A deaf person may be able to communicate with hearing people without an interpreter if reliable sign language recognition technology is made available to them. It can be used to generate voice or text, giving the mute greater independence. The study of Arabic sign language recognition is crucial. It facilitates better communication between the deaf and others in the community. This paper attempts to investigate the current Arabic sign language (ArSL) recognition approaches which are commonly and efficiently used for building the automatic translation and recognition of ArSL systems. Furthermore, the focus is given on highlighting the challenges and future work directions to explore the unsolved problems related to ArSL recognition systems.

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Section: Resnet152mentioning

confidence: 99%

Untitled

2024

INDJCSE

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“…Te system's recognition skills have been vastly improved using a decision tree, a Bayesian network, and Ada-boosting. In the study [59], the authors provided a novel ArSL recognition approach that makes direct use of microscopic images and is based on an unsupervised deep-learning method, a deep belief network (DBN). It has been used to recognize and sort the Arabic alphabet.…”

Section: Related Workmentioning

confidence: 99%

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

Moustafa,

Mohd Rahim,

Bouallegue

et al. 2023

Journal of Electrical and Computer Engineering

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Deaf and dumb people struggle with communicating on a day-to-day basis. Current advancements in artificial intelligence (AI) have allowed this communication barrier to be removed. A letter recognition system for Arabic sign language (ArSL) has been developed as a result of this effort. The deep convolutional neural network (CNN) structure is used by the ArSL recognition system in order to process depth data and to improve the ability for hearing-impaired to communicate with others. In the proposed model, letters of the hand-sign alphabet and the Arabic alphabet would be recognized and identified automatically based on user input. The proposed model should be able to identify ArSL with a rate of accuracy of 97.1%. In order to test our approach, we carried out a comparative study and discovered that it is able to differentiate between static indications with a higher level of accuracy than prior studies had achieved using the same dataset.

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“…Because education is an effort to develop and cultivate people both physically and mentally [35]. Some experts also think of education as a process of changing the attitude or behavior of a person or group of people in maturing themselves through teaching and practice [36]. According to H. Horne education is a continuous (eternal) process of higher adjustment for the human being who has developed physically and mentally.…”

Section: Introductionmentioning

confidence: 99%

“…Because the influence of technology is very large in the world of education such as the availability of various kinds of learning media [16]. And according to research conducted by M Hasan, the use of technology in the learning process for students can affect the academic performance of students in higher education [36], because in learning student learning achievement is influenced by internal and external conditions [46], internal conditions include physical, social, and psychological conditions, while external conditions include the environment and media in learning [17].…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to analyze and find out how the effects or influences of technology on Arabic language learning in universities [47]. And measuring the extent of the application of this technology in the learning process is highly valued [36]. Because the impact of this technology is felt to be very large for students in universities, especially in learning Arabic [48].…”

Section: Introductionmentioning

confidence: 99%

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The effect of technology on Arabic language learning in higher education

Ritonga,

Mudinillah,

Wasehudin

et al. 2024

EduLearn

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Technology is advancing and developing, and we cannot let go of our lives. One of them is in the world of education, the use of technology is often found. teachers combine technological devices in the learning process so that the learning atmosphere becomes more interesting, not only with increasingly open technological developments and the spread of information and knowledge throughout the world across the boundaries of space and time. Thus, technological developments have a positive impact on educators and students. In this study, researchers measured the extent to which the application of this technology in the learning process was highly valued. For this reason, this research aims to find out the impact of technology on Arabic language learning at the Batusangkar Institute State Islamic University. The method used in this research is a survey research method. Data collection uses observation, interviews and questionnaires. Furthermore, the expected result of this research is to find out the role or influence of technology on Arabic language learning activities in higher education. Based on the research results, it can be seen that there is an influence or influence of technology on Arabic language learning at the Batusangkar State Islamic Institute.

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Arabic Sign Language Characters Recognition Based on Deep Learning Approach and a Simple Linear Classifier

Cited by 10 publications

References 0 publications

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Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

The effect of technology on Arabic language learning in higher education

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