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Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

Author

Listed:
  • Ravinder Ahuja

    (Jaypee Institute of Information Technology Noida, Hansi, India)

  • Daksh Jain

    (Jaypee Institute of Information Technology Noida, Delhi, India)

  • Deepanshu Sachdeva

    (Jaypee Institute of Information Technology Noida, Delhi, India)

  • Archit Garg

    (Jaypee Institute of Information Technology Noida, Ghaziabad, India)

  • Chirag Rajput

    (Jaypee Institute of Information Technology Noida, New Delhi, India)

Abstract

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

Suggested Citation

  • Ravinder Ahuja & Daksh Jain & Deepanshu Sachdeva & Archit Garg & Chirag Rajput, 2019. "Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 10(3), pages 60-73, July.
    • Handle: RePEc:igg:jaci00:v:10:y:2019:i:3:p:60-73
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