Efficient deep learning models based on tension techniques for sign language recognition

Attia, Nehal F.; Ahmed, Mohamed T. Faheem Said; Alshewimy, Mahmoud A.M.

doi:10.1016/j.iswa.2023.200284

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…This study [23] aims to increase sign language identification accuracy. This will be done by developing three advanced deep-learning models utilizing YOLOv5x and attention.…”

Section: Related Workmentioning

confidence: 99%

Efficient YOLO Based Deep Learning Model for Arabic Sign Language Recognition

Ahmadi,

Mohammad,

Dawsari

2024

Preprint

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Verbal communication is the dominant form of self-expression and interpersonal communication. Speech is a considerable obstacle for individuals with disabilities, including those who are deaf, hard of hearing, mute, or nonverbal. Consequently, these individuals depend on sign language to communicate with others. Sign Language is a complex system of gestures and visual cues that facilitate the inclusion of individuals into vocal communication groups. In this manuscript a novel technique proposed using deep learning to recognize the Arabic Sign language (ArSL) accurately. Through this advanced system, the objective is to help in communication between the hearing and deaf community. The proposed mechanism relies on advanced attention mechanisms, and state-of-art Convolutional Neural Network (CNN) architectures with the robust YOLO object detection model that highly improves the implementation and accuracy of ArSL recognition. In our proposed method, we integrate the self-attention block, channel attention module, spatial attention module, and cross-convolution module into the features processing, and the ArSL recognition accuracy reaches 98.9%. The recognition accuracy of our method is significantly improved with higher detection rate. The presented approach showed significant improvement as compared with the conventional techniques with a precision rate of 0.9. For the mAP@0.5, the mAP score is 0.9909 while for the mAP@0.5:0.95 and the results tops all the state-of-the-art techniques. This shows that the model has the great capability to accurately detect and classify complex multiple ArSL signs. The model provides a unique way of linking people and improving the communication strategy while also promoting the social inclusion of deaf people in the Arabic region.

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“…This study [23] aims to increase sign language identification accuracy. This will be done by developing three advanced deep-learning models utilizing YOLOv5x and attention.…”

Section: Related Workmentioning

confidence: 99%

Efficient YOLO Based Deep Learning Model for Arabic Sign Language Recognition

Ahmadi,

Mohammad,

Dawsari

2024

Preprint

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Using LSTM to translate Thai sign language to text in real time

Jintanachaiwat,

Jongsathitphaibul,

Pimsan

et al. 2024

Discov Artif Intell

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Between 2019 and 2022, as the Covid-19 pandemic unfolded, numerous countries implemented lockdown policies, leading most corporate companies to permit employees to work from home. Communication and meetings transitioned to online platforms, replacing face-to-face interactions. This shift posed challenges for deaf or hearing-impaired individuals who rely on sign language, using hand gestures for communication. However, it also affected those who can hear clearly but lack knowledge of sign language. Unfortunately, many online meeting platforms lack sign language translation features. This study addresses this issue, focusing on Thai sign language. The objective is to develop a model capable of translating Thai sign language in real-time. The Long Short-Term Memory (LSTM) architecture is employed in conjunction with MediaPipe Holistic for data collection. MediaPipe Holistic captures keypoints of hand, pose, and head, while the LSTM model translates hand gestures into a sequence of words. The model’s efficiency is assessed based on accuracy, with real-time testing achieving an 86% accuracy, slightly lower than the performance on the test dataset. Nonetheless, there is room for improvement, such as expanding the dataset by collecting data from diverse individuals, employing data augmentation techniques, and incorporating an attention mechanism to enhance model accuracy.

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Unveiling the Power of Machine Learning and Deep Learning in Advancing American Sign Language Recognition

et al. 2024

2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS)

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Efficient deep learning models based on tension techniques for sign language recognition

Cited by 4 publications

References 22 publications

Efficient YOLO Based Deep Learning Model for Arabic Sign Language Recognition

Efficient YOLO Based Deep Learning Model for Arabic Sign Language Recognition

Using LSTM to translate Thai sign language to text in real time

Unveiling the Power of Machine Learning and Deep Learning in Advancing American Sign Language Recognition

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