Real-Time Sign Language Recognition System

Sen, Subhajit; Narang, Shreya; Gouthaman, P.

doi:10.1109/accthpa57160.2023.10083349

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…The third study proposes a system that gives 70%-80% accuracy using CNN and for training uses [19]pre-trained model SSD Mobile Net V2. This system is able to give [19]accurate results in [19]controlled light and intensity. As the dataset gets bigger, it can be taken to a large scale.…”

Section: Discussionmentioning

confidence: 99%

“…OpenCV is used to capture images of hand gestures using a webcam. Labelling is performed as the next step, using a [19]pre-trained model called [19]SSD Mobile Net v2 for sign recognition [19]. Capturing different images [19]of multiple [19]sign language symbols from various angles.…”

Section: Real Time Sign Language Detection [2021]mentioning

confidence: 99%

“…Labelling is performed as the next step, using a [19]pre-trained model called [19]SSD Mobile Net v2 for sign recognition [19]. Capturing different images [19]of multiple [19]sign language symbols from various angles. Bounding boxes are enclosed in the particular sign language that is selected from the entire image.…”

Section: Real Time Sign Language Detection [2021]mentioning

confidence: 99%

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A Review on Sign Language Recognition

2023

IRJMETS

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True impairment is the inability to converse with others. [1]Deaf and dumb people mostly utilise [1] sign language to [1] communicate inside [1]their group [1]and with other people. People who are hesitant to converse or hear use hand gestures to communicate in this language. Hands, eyes, facial emotions, and movement are all used as visual clues within this form of language. Even though sign language has grown to be increasingly widespread in recent years, it can still be difficult for non-signers to interact with signers. A technique for [2]sign language recognition is presented [2]in this study. Recognizing [2]sign language through actions [2]is referred to as sign language recognition. For this, capturing sign language gestures for words, numerals, and alphabets using a camera. The recorded images are processed using supervised algorithms and various data sets. The pre-trained models and the different layers of these algorithms are used for feature extraction and classification. The system will provide the equivalent sign language in text or speech as its output.

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Section: Discussionmentioning

confidence: 99%

Section: Real Time Sign Language Detection [2021]mentioning

confidence: 99%

Section: Real Time Sign Language Detection [2021]mentioning

confidence: 99%

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A Review on Sign Language Recognition

2023

IRJMETS

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“…Environments. Image processing in low light generates false positives, which directly afect the performance and results [171,172]. Te question "How can sign language detection systems on smartphones perform efectively in low-light conditions and noisy environments?"…”

Section: Low-light and Noisymentioning

confidence: 99%

Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches

Alam,

Hameed,

Ziar

2024

Advances in Human-Computer Interaction

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In this modern era of technology, most of the accessibility issues are handled with the help of smart devices and cutting-edge gadgets. Smartphones play a crucial role in addressing various accessibility challenges, including voice recognition, sign language detection and interpretation, navigation systems, speech-to-text conversion, and vice versa, among others. They are computationally powerful enough to handle and run numerous machine and deep learning applications. Among various accessibility challenges, speech disorders represent a disability where individuals struggle to communicate verbally. Similarly, hearing loss is a disability that impairs an individual’s ability to hear, necessitating reliance on gestures for communication. A significant challenge encountered by people with speech disorders, hearing loss, or both is their inability to effectively convey or receive messages from others. Hence, these individuals heavily depend on the sign language (a gesture-based communication) method, typically involving hand movements and expressions. To the best of our knowledge, there are currently no comprehensive review and/or survey articles available that cover the literature on speech disabilities and sign language detection and interpretation via smartphones utilizing machine learning and/or deep learning approaches. This study fills the gap in the literature by analyzing research publications on speech disabilities, published from 2012 to July 2023. A rigorous search and standard strategy for formulating the literature along with a well-defined theoretical framework for results and findings have been used. The paper has implications for practitioners and researchers working in accessibilities in general and smart/intelligent gadgets and applications for speech-disabled people in specific.

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“…Moreover, research efforts [5][6] have explored real-time sign language recognition models using OpenCV, Keras, and deep neural networks. Several papers [7][8][9][10] have contributed to efficient gesture identification, transfer learning for Indian Sign Language (ISL) gestures, and Indo-Russian sign gesture recognition.…”

mentioning

confidence: 99%

Flex Sensor and Machine Learning-Based Mute Communication System

Ghosh¹

2023

IJSREM

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Individuals facing speech impairments encounter significant communication challenges. To address this, we have developed a groundbreaking system that seamlessly translates custom hand gestures into written text and spoken language. Our system combines flex sensors and state-of-the-art machine learning techniques, empowering individuals with speech impairments to express themselves effectively and improve their overall communication experience. By connecting flex sensors to an Arduino board and utilizing MATLAB for robust data collection, analysis, and processing, we achieve a highly accurate gesture recognition system. Through meticulous steps of data labeling, rigorous training of a machine learning model, and thorough accuracy evaluations, our system recognizes a wide range of gestures, including numbers 0-9, letters A-Z, and 10 Kannada language gestures conveying meaningful phrases. The hand gesture recognition system achieved notable performance with an accuracy of 83.5%, precision of 88.6%, recall of 95.1%, and F1 Score of 91.7%. Key Words: speech impairments, hand gestures, gesture recognition, flex sensors,

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Real-Time Sign Language Recognition System

Cited by 4 publications

References 9 publications

A Review on Sign Language Recognition

A Review on Sign Language Recognition

Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches

Flex Sensor and Machine Learning-Based Mute Communication System

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