International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Alphabet Recognition of American Sign Language Using Machine Learning

Author(s) Saloni Tiwari, Deepti Arora, Riya Garg, Shriya, Lester Sharma
Country India
Abstract A major challenge faced by our society is the difficulty individuals with disabilities encounter in expressing their emotions to others. People with disabilities often depend on sign (gesture) languages for communication. This project aims to develop a model capable of recognizing and translating sign language alphabets from hand gestures into text and audio. The primary objective is to enhance communication between individuals with hearing impairments and the broader society. We assessed our model's performance using a publicly accessible Indian Sign Language (ISL) dataset. For image classification, we employed Convolutional Neural Networks (CNNs) with the Inception V3 architecture. Hand gestures are captured through webcams, and our model identifies the corresponding alphabet for each gesture. This project seeks to overcome current challenges in Sign Language Recognition and aims to enhance its effectiveness and efficiency.
Keywords Sign Language with Hand Gesture, Gesture Recognition, Human Computer Interaction also with Sign Language and Recognition.
Field Engineering
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-07-15
Cite This Alphabet Recognition of American Sign Language Using Machine Learning - Saloni Tiwari, Deepti Arora, Riya Garg, Shriya, Lester Sharma - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.23100
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.23100
Short DOI https://doi.org/gt4gnb
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