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Synapse
April 8, 20260 citationsOpen Access

Sign Language Interpreter Using Deep CNN

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AYAkshay YadavAPAkhilesh Kumar PrasadPSP. Deep Sai

Key Points

  • The study aims to create an accurate real-time interpreter for sign language using deep convolutional neural networks.
  • Developed a sign language interpreter system using deep CNN.
  • Captured hand gestures through a webcam.
  • Classified gestures into corresponding alphabet letters.
  • Evaluated the system's accuracy in real-time settings.
  • Achieved high accuracy in classifying sign language gestures.
  • Enabled effective communication for hearing and speech-impaired users.

Abstract

This paper presents a real-time Sign Language Interpreter using Deep Convolutional Neural Networks (CNN). The system captures hand gestures using a webcam and classifies them into alphabets. The proposed system achieves high accuracy and helps hearing and speech-impaired people communicate effectively.

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Cite This Study

Yadav et al. (2026) studied this question.

synapsesocial.com/papers/69d5f14b74eaea4b11a7ae5bhttps://doi.org/10.5281/zenodo.19441279
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