Applying deep neural networks for the automatic recognition of sign language words: A communication aid to deaf agriculturists

Venugopalan, Adithya; Rajesh, R.

doi:10.1016/j.eswa.2021.115601

Cited by 36 publications

(14 citation statements)

References 38 publications

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“…Sign language recognition, referred to as SLR, can be defined as the detection and systematic conversion of various sign language gestures to a language that the general public can understand, such as words, symbols, or speech [ 15 ]. These sign language gestures consist of hand movements, facial expressions, and even the movement of the entire body.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Human actions and sign language recognition are among the trending issues in data science and artificial intelligence, and have many applications [ 23 ]. The initial and most prioritized goal [ 15 ] for the development of human action recognition is to narrow the communication gap between the active speech-impaired community and the general public [ 24 ]. Action recognition is to narrow the communication gap between the active speech impaired community and the regular public members [ 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This will enable different community members to live in harmony thanks to the greater ease of sharing feelings and personal expression. The narrowing of this gap will be very useful in helping more people to better share their ideas and knowledge, aiding different production sectors such as industry and agriculture [ 15 ]. The application of sign language and human action recognition has found many applications in different areas over time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This happens in many cases and in many different fields, inconveniencing people from the active speech-impaired community. For instance, people may be unable to communicate messages to the authorities [ 15 ]. In other cases, they may be hindered from participating in various economic activities such as agriculture despite having all of the necessary skills and abilities due to their inability to communicate effectively with others [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, over 120 sign languages are used worldwide, and it is not straightforward for society at large to learn these languages and communicate effectively using them [ 4 ]. Researchers and engineers have tried to develop sign language recognition systems to narrow this gap, for example, a conduct-based system known as a glove sensor [ 16 ]; however, these systems require hardware setups that are complex and relatively expensive, and therefore they are not preferred [ 15 ]. Researchers and engineers have started applying vision-based systems, which are relatively cheaper as they only use cameras [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Sign and Human Action Detection Using Deep Learning

2022

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Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.

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Section: Literature Reviewmentioning

confidence: 99%