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Static Hand Sign Recognition Using Wavelet Transform and Convolutional Neural Network

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Second International Conference on Sustainable Technologies for Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1235))

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Abstract

Hand gestures play a crucial role in helping the deaf and hearing impairment community to communicate smoothly. Consequently, a robust hand sign recognition system will help the first responders to better communicate and understand those people, who cannot use the spoken languages. Therefore, in this research paper, we propose a novel approach for static hand sign recognition using convolutional neural network as the classifier and discrete wavelet transform as the feature extractor. We then implement our proposed approach using Python programming language. Moreover, we use the MNIST dataset, which is one of the publicly available datasets available at Kaggle website to further implement our research approach. Furthermore, we compared our approach with Hu moments, Zernike moments (Degree 8), and Krawtchouk moments feature extraction techniques along with SVM Linear, Radial, Random Forest, and K-Nearest Neighbor classifiers. As a result, our approach yields the 99.1% accuracy that outperforms the accuracies of above-discussed feature extraction methods and classifiers.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India

    Rohit Beniwal, Bhavya Nag, Avneesh Saraswat & Parth Gulati

Authors
  1. Rohit Beniwal
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  2. Bhavya Nag
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  3. Avneesh Saraswat
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  4. Parth Gulati
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Editor information

Editors and Affiliations

  1. The PNG University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. CHRIST (Deemed to be University), Bangalore, India

    Ramesh Chandra Poonia

  3. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  4. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

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Beniwal, R., Nag, B., Saraswat, A., Gulati, P. (2022). Static Hand Sign Recognition Using Wavelet Transform and Convolutional Neural Network. In: Luhach, A.K., Poonia, R.C., Gao, XZ., Singh Jat, D. (eds) Second International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1235. Springer, Singapore. https://doi.org/10.1007/978-981-16-4641-6_13

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