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September 5, 2025Scientific ReportsOpen Access

Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals

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The study achieved an impressive accuracy of 98.95% in sign language recognition, outperforming existing methods.
Utilizing the Harris Hawk Optimization approach, the technique adjusts the Bi-LSTM model's parameters for better classification.
Image pre-processing with bilateral filtering plays a crucial role in enhancing the input dataset for effective recognition.
Deep learning models, particularly ResNet-152 and Bi-LSTM, are pivotal in the success of this sign language recognition technique.

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Implication

Novel technique significantly improves accuracy in sign language recognition for hearing-impaired individuals, suggesting better community integration.

Cite This Study

Almjally et al. (2025) studied this question.

synapsesocial.com/papers/68bb5f3e6d6d5674bcd0365d https://doi.org/10.1038/s41598-025-09106-8

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Implication

Novel technique significantly improves accuracy in sign language recognition for hearing-impaired individuals, suggesting better community integration.

Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals

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Discussion

Implication

Cite This Study

Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals

Key Points

Discussion

Implication

Cite This Study

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