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
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21246
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Copyright: © Institute of Research and Journals
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Published on 2025-01-08 |
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