An Efficient Approach for Interpretation of Indian Sign Language using Machine Learning

Authors: Rasha Anjum, Amatun Noor Sadaf, Maheen Sami, Kamel Alikhan Siddiqui

DOI: https://doi.org/10.37082/IJIRMPS.v11.i1.230316

Short DOI: https://doi.org/gspr3h

Country: India

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Abstract: Non-verbal communication involves the usage of Sign Language. The sign language is used by people with hearing / speech disabilities to express their thoughts and feelings. But normally, people find it difficult to understand the hand gestures of the specially challenged people as they do not know the meaning of the sign language gestures. Usually, a translator is needed when a speech / hearing impaired person wants to communicate with an ordinary person and vice versa. In order to enable the specially challenged people to effectively communicate with the people around them, a system that translates the Indian Sign Language (ISL) hand gestures of numbers (1-9), English alphabets (A-Z) and a few English words to understandable text and vice versa has been proposed in this paper. This is done using image processing techniques and Machine Learning algorithms. Different neural network classifiers are developed, tested and validated for their performance in gesture recognition and the most efficient classifier is identified.

Keywords: Machine Learning, Python, ISL and Gesture Recognition

Paper Id: 230316

Published On: 2023-01-04

Published In: Volume 11, Issue 1, January-February 2023

Cite This: An Efficient Approach for Interpretation of Indian Sign Language using Machine Learning - Rasha Anjum, Amatun Noor Sadaf, Maheen Sami, Kamel Alikhan Siddiqui - IJIRMPS Volume 11, Issue 1, January-February 2023. DOI 10.37082/IJIRMPS.v11.i1.230316

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