R. Anurag Upadhyay, G. Ananda Kumar, R. Ranjith, G. Ram Sankar
Machine Learning Algorithms, Prediction, Reliability, Prediction model, Regression
Sign language serves as a natural and profound means of communication, yet its accessibility is hindered by the scarcity of proficient interpreters. To address this challenge, we propose a real-time method for fingerspelling recognition in American Sign Language (ASL) utilizing neural networks. Our approach involves preprocessing the hand gesture through a filtering mechanism before classification using a neural network model. We present experimental results demonstrating a high accuracy of 95.7% for recognizing the 26 letters of the alphabet. By leveraging machine learning techniques, our method offers a practical solution to enhance communication accessibility for individuals who rely on sign language. This research contributes to the advancement of assistive technology and underscores the potential of neural networks in facilitating inclusive communication environments
Article Details
Unique Paper ID: 162591

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 444 - 450
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