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Deaf and mute people do have not many options for communicating with a hearing person. With the potential of Artificial Intelligence in all aspects of our lives the scope to help people with hearing disabilities has increased. We can use Deep learning and Computer vision to make a difference in this cause. Our proposed system can translate as quickly as the person speaks and translates any sign language. It will help to remove communication barriers between the healthy and disabled at the workplace with a compellingly fast and economical solution. In this work a real-time hand gesture recognizer using the Media Pipe framework and TensorFlow in Python and OpenCV is proposed. The TensorFlow Opensource API enables running on-device machine learning models on Chrome and other consumer devices. CPU, GPU, and dedicated ML accelerator blocks, like TPUs, drastically improves computing power.

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Implementation of Hand Sign Classifier Using Deep Neural Network (DNN)

  • 会议论文

  • 发表日期：

    2026年

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  • Sobhana Obulareddy,
  • Rashmi Kapoor

摘要

Deaf and mute people do have not many options for communicating with a hearing person. With the potential of Artificial Intelligence in all aspects of our lives the scope to help people with hearing disabilities has increased. We can use Deep learning and Computer vision to make a difference in this cause. Our proposed system can translate as quickly as the person speaks and translates any sign language. It will help to remove communication barriers between the healthy and disabled at the workplace with a compellingly fast and economical solution. In this work a real-time hand gesture recognizer using the Media Pipe framework and TensorFlow in Python and OpenCV is proposed. The TensorFlow Opensource API enables running on-device machine learning models on Chrome and other consumer devices. CPU, GPU, and dedicated ML accelerator blocks, like TPUs, drastically improves computing power.