Abstract
There is a long drawn communication barrier between normal people and deaf-mute community. Sign language is a major tool of communication for hearing impaired people. The goal of this work is to develop a Convolutional Neural Network (CNN) based Indian sign language classifier. CNN models with combination of different hidden layers are analysed and the model giving highest accuracy is selected. Further synthetic data is generated using Conditional Generative Adversarial Network (CGAN), in order to improve classification accuracy.
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Notes
- 1.
The dataset used in this work can be found here.
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Charan, M.G.K.S. et al. (2022). Sign Language Recognition Using CNN and CGAN. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_33
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DOI: https://doi.org/10.1007/978-981-19-1012-8_33
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