Abstract
Hand gesture recognition is an important aspect of human computer interaction. It forms the basis of sign language for the visually impaired people. This work proposes a novel hand gesture recognizing system for the differently-abled persons. The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset by employing Python and Keras libraries. Furthermore, the result is validated by the NUS dataset, consisting of 10 classes of hand gestures, fed to the model as the validation set. Afterwards, a testing dataset of 10 classes is built by employing Google’s open source Application Programming Interface (API) that captures different gestures of human hand and the efficacy is then measured by carrying out experiments. The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.
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The authors are thankful to the reviewers for their suggestions in successfully completing the work.
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Sharma, S., Ghosh, A., Subudhi, S. (2022). Hand Sign Language Detection Using Deep Learning. In: Sahoo, J.P., Tripathy, A.K., Mohanty, M., Li, KC., Nayak, A.K. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-16-4807-6_47
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DOI: https://doi.org/10.1007/978-981-16-4807-6_47
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