Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language

Authors

  • Ramesh M. Badiger Department of Computer Science and Engineering, Tontadarya College of Engineering, India
  • Rajesh Yakkundimath Department of Computer Science and Engineering, KLE Institute of Technology, India
  • Guruprasad Konnurmath School of Computer Science and Engineering, KLE Technological University, Hubballi, India
  • Praveen M. Dhulavvagol School of Computer Science and Engineering, KLE Technological University, Hubballi, India
Volume: 14 | Issue: 2 | Pages: 13255-13260 | April 2024 | https://doi.org/10.48084/etasr.6864

Abstract

This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves the best performance with a test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes.

Keywords:

sign language, age group, gesture identification, transfer learning, Inception-V3, VGG-16, ResNet-50

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How to Cite

[1]
R. M. Badiger, R. Yakkundimath, G. Konnurmath, and P. M. Dhulavvagol, “Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13255–13260, Apr. 2024.

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