International Journal of Advance Computational Engineering and Networking (IJACEN)
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Volume-12,Issue-9  ( Sep, 2024 )
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Statistics report
Feb. 2025
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 141
Paper Published : 1672
No. of Authors : 4423
  Journal Paper


Paper Title :
Impact of Demonstration Variability on Deep Neural Network Performance in Sign Language Recognition

Author :Nurzada Amangeldy, Bekbolat Kurmetbek, Gazizova Nazerke, Saule Kudubayeva, Raushan Amangeldy

Article Citation :Nurzada Amangeldy ,Bekbolat Kurmetbek ,Gazizova Nazerke ,Saule Kudubayeva ,Raushan Amangeldy , (2024 ) " Impact of Demonstration Variability on Deep Neural Network Performance in Sign Language Recognition " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 39-44, Volume-12,Issue-9

Abstract : This article investigates the impact of demonstration variability on the accuracy of recognizing sign language words and alphabet using deep neural networks. The primary focus is on evaluating the effectiveness of various deep learning architectures. The key contribution of this work lies in identifying the importance of multimodal approaches and model adaptation to enhance the accuracy of gesture recognition under conditions of low variability, specifically using the alphabet as an example. This is crucial for the development of automatic sign language and alphabet recognition systems. It is particularly critical because, unlike dynamic words, proper nouns demonstrated through the alphabet must be recognized with consistent efficiency during continuous recognition. Keywords - gesture recognition, variability, lip reading, multimodal approaches, clustering, model adaptation, real-time recognition

Type : Research paper

Published : Volume-12,Issue-9


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