DRISHYAM: An Interpreter for Deaf and Mute using Single Shot Detector Model

Gupta, Amit Munna; Koltharkar, Suyash Sagar; Patel, Hemil Deepak; Naik, Sunita

doi:10.1109/icaccs54159.2022.9785254

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The authors reported a hand gesture detection accuracy of 93%. A CNN-based prototype for hand gesture detection is presented in [26]. This prototype also provides a training option and support for multiple languages.…”

Section: System Level Reviewmentioning

confidence: 99%

“…Hardware/Software Summary [2] LMD, Android app D-M to ND-M to D-M, Single SLD [3] PC, Harris algorithm D-M to ND-M to D-M, Single SLD [4] Raspberry Pi D-M to ND-M, Single SLD [5] LMD, LSTM Neural networks D-M to ND-M, Single SLD [6] LMD, (GMM, HMM, DTW) D-M to ND-M, Single SLD [7] PC, NN algorithms D-M to ND-M, Single SLD [8] PC, Arduino, Gloves, Flex sensors Calibration required for the custom-made product, D-M to ND-M, Single SLD [10] Arduino, Gloves, Sensors Calibration required for the custom-made product, D-M to ND-M, Single SLD [12] PC, TensorFlow D-M to ND-M, Single SLD [13] PC, Various ML algorithms D-M to ND-M to D-M, Single SLD [14] PC, TensorFlow, CNN D-M to ND-M, Single SLD [15] Android D-M to ND-M, Multi SLD [16] Android D-M to ND-M, Single SLD [17] PC, CNN D-M to ND-M, Single SLD [18] Arduino, Gloves, Flex sensors Calibration required for the custom-made product, D-M to ND-M, Single SLD [19] PC NN D-M to ND-M, Single SLD [20] Gloves, Flex sensors, Voice recorder, Matlab D-M to ND-M, Single SLD [21] PC, CNN D-M to ND-M, Single SLD [22] CNN D-M to ND-M to D-M, Single SLD [23] Gloves, Sensors, Arduino, KNN D-M to ND-M, Single SLD [24] PC-based, PCA D-M to ND-M, Single SLD [25] Arduino, Gloves, Sensors, KNN Calibration required for the custom-made product, D-M to ND-M, Single SLD [26] PC-based, CNN D-M to ND-M, Single SLD [27] Android application D-M to ND-M, Multi SLD [28] PC, CNN D-M to ND-M, Single SLD [29] Android D-M to ND-M to D-M, Single SLD…”

Section: Referencesmentioning

confidence: 99%

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A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

et al. 2023

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This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and presented in this manuscript. This dataset includes three sign language datasets, i.e., American Sign Language (ASL), Pakistani Sign Language (PSL), and Spanish Sign Language (SSL). The proposed system automatically detects the sign language and converts it into an audio message for the ND-M. Similarities between the three sign languages are also explored, and further research can be carried out in order to help create more datasets, which can be a combination of multiple sign languages. The ND-M can communicate by recording their speech, which is then converted into text and hand gesture images. The system can be upgraded in the future to support more sign language datasets. The system also provides a training mode that can help D-M individuals improve their hand gestures and also understand how accurately the system is detecting these gestures. The proposed system has been validated through a series of experiments resulting in hand gesture detection accuracy exceeding 95%.

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Section: System Level Reviewmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

et al. 2023

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“…Based on the Regulation of the Ministry of Education and Culture of the Republic of Indonesia Number 061/U/1994 concerning Standardization of the Indonesian Sign Language System for the Deaf, SIBI is a sign language that has been inaugurated by the government in accordance with the applicable curriculum and as a teaching standard in Special Schools (SLB). In making this deaf sign language detection application using the Single Shot Detection (SSD) method for making detection applications, hand movements are used to detect the letters that you want to use in order to know the letters you want to say (Gupta et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

Iskandar¹,

Yel²,

Sitohang³

2022

JAETS

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Sign Language is a language that prioritizes manual communication, body language, and lip movements, instead of sound, to communicate. Deaf people are the main group who use this language, usually by combining hand shape, orientation and movement of the hands, arms, and body, and facial expressions to express their thoughts. Therefore, the researcher created an image recognition program in sign language using the Single Shot Detection (SSD) method, which is a convolution activity by combining several layers of preparation, by utilizing several components that move together and are motivated by a biological sensory system. The letters used in making sign language programs use the letters of the alphabet (az). This sign language detection programming that runs on the Google Collaboratory application

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A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

et al. 2022

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Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. The system is modular and can be enhanced by adding data to support more languages in the future. A supervised machine learning dataset is defined and created that provides automated multi-language communication between the DnM and NDnM people. It is expected that this system will support DnM people in communicating effectively with others and restoring a feeling of normalcy in their daily lives. The hand gesture detection accuracy of the system is more than 90% for most, while for certain scenarios, this is between 80% and 90% due to variations in hand gestures between DnM people. The system is validated and evaluated using a series of experiments.

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DRISHYAM: An Interpreter for Deaf and Mute using Single Shot Detector Model

Cited by 4 publications

References 8 publications

A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

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