Tunisian Sign Language Recognition System of Static Two-Handed Asymmetrical Signs using Deep Transfer Learning

Domain:

natural language processing

Record type:

dataset

Creator:

EmnHaiNab

Publisher:

Mad

Host:

Deaf and Hard of Hearing people use Sign Languages in the interaction among themselves and among hearing people. The automatic recognition of Static Two-Handed Asymmetrical signs is a hard operation, since it involves the implementation of complex processing system for providing image perception. In this paper, we produce a dataset of 2000 images containing 12 Two-handed Asymmetrical Tunisian Signs and utilize transfer learning for automatic recognition, achieving 98.29 % Accuracy. The simulations prove that this best Accuracy value is yielded by the Xception model when combined with the Adagrad optimizer, which indicates that our approach achieves high results despite using a small Dataset.

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Tunisian Sign Language Recognition System of Static Two-Handed Asymmetrical Signs using Deep Transfer Learning

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