A Study on Indian Sign Language Recognition using Deep Learning Approach |
Author(s): |
Shaon Bandyopadhyay , BCREC |
Keywords: |
Indian Sign Language (ISL), Deep Learning, Classification, ResNet |
Abstract |
Sign language recognition has been a well-researched topic for American Sign Language (ASL), but regarding Indian Sign Language (ISL), few research works have been published. In this paper, we propose a new method for Indian Sign Language Recognition using Residual Neural Networks (ResNet). ResNet is a deep learning architecture that is computationally expensive and normally used to provide high accuracy in classification problems. The proposed method can recognize static hand signs of 24 distinct English alphabets (A, B, C, D, E, F, G, H, I, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y) with total 4972 images and achieved 99.26% recognition rate on the validation dataset. |
Other Details |
Paper ID: IJSRDV8I40836 Published in: Volume : 8, Issue : 4 Publication Date: 01/07/2020 Page(s): 582-585 |
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