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An Investigation and Observational Remarks on Conventional Sign Language Recognition

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Inventive Systems and Control

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 672))

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Abstract

In today’s world, communication of speech and hearing impaired person has been facilitated with different technologies. In the world, there are about 300 million people are deaf, 285 million are blind and 1 million are dumb, as per the World Health Organization. Sign language is a tool to communicate with speech and hearing impaired people. Recognition of sign language is a challenge for researchers from many years that have to be implemented as a system for various sign languages. Each system has its own limitations and difficult to be used commercially. Researchers have done their research in various ways to simplify the recognizing of sign languages with limited database. Researchers are trying to do research with their own large database. Through this paper, we review the different sign language recognition approaches and try to find the best method that has been used. This helps the researchers to retrieve more information to develop the sign language recognition systems using current and advanced technologies in future.

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References

  1. Ajay S, Potluri A, George SM, Gaurav R, Anusri S (2021) Indian sign language recognition using random forest classifier

    Google Scholar 

  2. Mangla FU, Bashir A, Lali I, Bukhari AC, Shahzad B (2020) A novel key-frame selection-based sign language recognition framework for the video data

    Google Scholar 

  3. Walizad ME, Hurroo M (2020) Sign language recognition system using convolutional neural network and computer vision

    Google Scholar 

  4. Shirbhate RS, Shinde VD, Metkari SA, Borkar PU, Khandge MA (2020) Sign language recognition using machine learning algorithm

    Google Scholar 

  5. Sharma S, Gupta R, Kumar A (2020) Trbaggboost: an ensemble-based transfer learning method applied to Indian sign language recognition

    Google Scholar 

  6. Agarwal S, Patel F, Chaturvedi P, Asha S (2018) A novel approach for communication among blind, deaf and dumb people

    Google Scholar 

  7. Tolentino LKS, Juan ROS, Thio-ac AC, Pamahoy MAB, Forteza JRR, Garcia XJO (2019) Static sign language recognition using deep learning

    Google Scholar 

  8. Mahesh Kumar NB (2018) Conversion of sign language into text

    Google Scholar 

  9. Boukdir A, Benaddy M, Ellahyani A, Meslouhi OE, Kardouchi M (2021) Isolated video-based Arabic sign language recognition using convolutional and recursive neural networks

    Google Scholar 

  10. Sharma A, Sharma N, Saxena Y, Singh A, Sadhya D (2020) Benchmarking deep neural network approaches for Indian sign language recognition

    Google Scholar 

  11. Kaur B, Joshi G, Vig R (2017) Indian sign language recognition using Krawtchouk moment-based local features

    Google Scholar 

  12. da Silva EP, Costa PD, Kumada KM, De Martino JM (2021) Facial action unit detection methodology with application in Brazilian sign language recognition

    Google Scholar 

  13. Gangrade J, Bharti J (2020) Vision-based hand gesture recognition for Indian sign language using convolution neural network

    Google Scholar 

  14. Gangrade J, Bharti J, Mulye A (2020) Recognition of Indian sign language using ORB with bag of visual words by Kinect sensor

    Google Scholar 

  15. Myagila K, Kilavo H (2021) A comparative study on performance of SVM and CNN in Tanzania sign language translation using image recognition

    Google Scholar 

  16. Potnis M,  Raul D,  Inamdar M (2021) Recognition of Indian sign language using machine learning algorithms

    Google Scholar 

  17. Baranwal N, Singh AK, Nandi GC (2017) Development of a framework for human–robot interactions with Indian sign language using possibility theory

    Google Scholar 

  18. Areeb QM, Nadeem M (2021) Deep learning based hand gesture recognition for emergency situation: a study on Indian sign language

    Google Scholar 

  19. Rajyashree, Deepak O, Rengaswamy N, Vishal KS (2019) Communication assistant for deaf, dumb and blind

    Google Scholar 

  20. Sharma S, Gupta R, Kumar A (2021) Continuous sign language recognition using isolated signs data and deep transfer learning

    Google Scholar 

  21. Sharma S, Singh S (2021) Recognition of Indian sign language (ISL) using deep learning model

    Google Scholar 

  22. Raghuveera T, Deepthi R, Mangalashri R, Akshaya R (2020) A depth-based Indian sign language recognition using Microsoft Kinect

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Master of Computer Applications, Vidya Vikas Institute of Engineering and Technology, Mysuru, Karnataka, India

    Thouseef Ulla Khan

  2. Department of Master of Computer Applications, VTU—Research Centre, Nitte Meenakshi Institute of Technology, Yelahanka, Bengaluru, Karnataka, India

    Thouseef Ulla Khan

  3. Department of Master of Computer Applications, Nitte Meenakshi Institute of Technology, Yelahanka, Bengaluru, Karnataka, India

    M. R. Dileep

Authors
  1. Thouseef Ulla Khan
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  2. M. R. Dileep
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Corresponding author

Correspondence to Thouseef Ulla Khan .

Editor information

Editors and Affiliations

  1. Computer Science and Design, Dayananda Sagar College of Engineering, Bengaluru, India

    V. Suma

  2. University of Haute Alsace, Mulhouse, France

    Pascal Lorenz

  3. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Zubair Baig

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Khan, T.U., Dileep, M.R. (2023). An Investigation and Observational Remarks on Conventional Sign Language Recognition. In: Suma, V., Lorenz, P., Baig, Z. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-99-1624-5_33

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