Sign language recognition system using machine learning

Authors: Dr. Atul S. Choudhary, KUSHAL SHARMA, ANISH SHINDE, SAKSHI BELE, ASHWINI SHINDE

Country: India

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Abstract: Sign language is a vital form of communication for the deaf and dumb community. To bridge the communication gap between sign language users and non-signers, we present a novel system for real-time sign language detection using a standard web camera. This system's goal is to recognize sign language gestures performed in front of the camera, then converting the gestures or signs into voice output and displaying the corresponding text on screen. It first identifies the signer’s hand gestures and recognizing key markers that represent signs. The system then employs the machine learning algorithms to translate those markers into sign language vocabulary. After successful detection of the sign, the system provides simultaneous output in two ways: voice and on-screen text. The voice output enables real-time interpretation for person who may not be familiar with sign language, at the same time the on-screen text serves as a visual reference. This two way output model ensures accessibility and inclusivity for a wider audience. By integrating this system into webcams and other devices with cameras, we aim to enhance the communication capabilities of the deaf and hard of hearing community, enabling them to interact more effectively with hearing individuals. Additionally, this technology can find its usefulness in education, healthcare, and other domains, fostering better understanding for sign language users.

Keywords: Sign language, Deaf and hard of hearing, Real-time, Sign language detection, Web camera, Computer vision.

Paper Id: 230666

Published On: 2024-05-25

Published In: Volume 12, Issue 3, May-June 2024

Cite This: Sign language recognition system using machine learning - Dr. Atul S. Choudhary, KUSHAL SHARMA, ANISH SHINDE, SAKSHI BELE, ASHWINI SHINDE - IJIRMPS Volume 12, Issue 3, May-June 2024.

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