RETRACTED ARTICLE: Smart communication using tri-spectral sign recognition for hearing-impaired people

Kanisha, B.; Mahalakshmi, V.; Baskar, M.; Vijaya, K.; Kalyanasundaram, P.

doi:10.1007/s11227-021-03968-1

Cited by 17 publications

(2 citation statements)

References 13 publications

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“…Sign language recognition involves creating an assistive system that can automatically transform an input sign into the voice or text that corresponds to it (Mittal et al, 2019). Therefore, the sign language recognition system is effective in eliminating the communication gap between communities of hearing and non-hearing individuals, and it opens a new avenue for applications that are based on human-computer interaction (Kanisha et al, 2022;Rakesh et al, 2021;Itkarkar et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Gesture Recognition for Communication with the Deaf and Dumb

Yavanamandha,

Keerthana,

Jahnavi

et al. 2023

IJERR

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A deep learning model specifically designed to recognize signs in sign language is the foundation of the Sign Language Recognition system. Sign Language is a visual language used by the deaf and hard of hearing community to communicate with one another and the general public. Sign language is a kind of nonverbal communication based on the use of hand gestures. The ability to communicate socially and emotionally is greatly aided when the speech and hearing challenged have access to sign language. The model developed in this paper captures the images through live web cam and displays the sign language meaning on the screen as text output. The model is trained and built by deep learning framework using Convolution Neural Networks (CNN) in this work. The model is trained with images of hand gestures captured through webcam using Computer Vision and then after successful training, the system performs recognition process through matching parameters for a given input gesture and finally displays the sign language meaning of the gesture as text output on the screen.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Gesture Recognition for Communication with the Deaf and Dumb

Yavanamandha,

Keerthana,

Jahnavi

et al. 2023

IJERR

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“…Sign language recognition (SLR) develops an assistive system that automatically converts an input sign into its corresponding speech or text 13 . Thus, the SLR system is useful for overcoming the communication gap between hearing and nonhearing communities and creates a new path for human-computer interaction-based applications 14 – 18 . The major challenge to developing a continuous SLR system is finding a modeling prototype that acquires the sign gesture and its corresponding text.…”

Section: Introductionmentioning

confidence: 99%

An integrated mediapipe-optimized GRU model for Indian sign language recognition

Subramanian

Olimov

Naik

et al. 2022

Sci Rep

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Sign language recognition is challenged by problems, such as accurate tracking of hand gestures, occlusion of hands, and high computational cost. Recently, it has benefited from advancements in deep learning techniques. However, these larger complex approaches cannot manage long-term sequential data and they are characterized by poor information processing and learning efficiency in capturing useful information. To overcome these challenges, we propose an integrated MediaPipe-optimized gated recurrent unit (MOPGRU) model for Indian sign language recognition. Specifically, we improved the update gate of the standard GRU cell by multiplying it by the reset gate to discard the redundant information from the past in one screening. By obtaining feedback from the resultant of the reset gate, additional attention is shown to the present input. Additionally, we replace the hyperbolic tangent activation in standard GRUs with exponential linear unit activation and SoftMax with Softsign activation in the output layer of the GRU cell. Thus, our proposed MOPGRU model achieved better prediction accuracy, high learning efficiency, information processing capability, and faster convergence than other sequential models.

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A deep learning approach to dysarthric utterance classification with BiLSTM-GRU, speech cue filtering, and log mel spectrograms

Mehra,

Ranga,

Agarwal

2024

J Supercomput

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RETRACTED ARTICLE: Smart communication using tri-spectral sign recognition for hearing-impaired people

Cited by 17 publications

References 13 publications

Machine Learning-Based Gesture Recognition for Communication with the Deaf and Dumb

Machine Learning-Based Gesture Recognition for Communication with the Deaf and Dumb

An integrated mediapipe-optimized GRU model for Indian sign language recognition

A deep learning approach to dysarthric utterance classification with BiLSTM-GRU, speech cue filtering, and log mel spectrograms

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