American Sign Language Classification using CNNs: A Comparative Study

Authors

  • Samrat Kumar Adhikari Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Pranav Neupane Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Shyama Mainali Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Utsarga Regmi Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal
  • Pralhad Chapagain Department of Computer and Electronics Engineering, Kantipur Engineering College, Dhapakhel, Lalitpur, Nepal

DOI:

https://doi.org/10.3126/injet.v1i2.66704

Keywords:

American Sign Language, Deep Learning, Convolutional Neural Network, Transfer Learning, Image Classification, Image Processing

Abstract

American Sign Language (ASL) classification is crucial in facilitating communication for individuals with hearing impairments. Traditional methods rely heavily on manual interpretation, which can be time-consuming and error-prone. Inspired by the success of deep learning techniques in image processing, the paper explores the application of Convolutional Neural Networks (CNNs) for ASL classification. The paper presents a CNN architecture tailored specifically for this task and investigates the effectiveness of transfer learning by leveraging four pre-trained models: VGG16, InceptionV3, ResNet50, and DenseNet121. A comparative analysis of these architectures has been presented in this paper. The experimental results show that the customized CNN model outperformed other models with a testing accuracy of 99.93% when provided with testing set images. Consequently, it is concluded that customized CNN outshines other models in accurately classifying sign languages.

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Published

2024-06-24

How to Cite

Kumar Adhikari, S., Neupane, P., Mainali, S., Regmi, U., & Chapagain, P. (2024). American Sign Language Classification using CNNs: A Comparative Study. International Journal on Engineering Technology, 1(2), 283–295. https://doi.org/10.3126/injet.v1i2.66704

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Section

Articles