Intelligent hybrid hand gesture recognition system using deep recurrent neural network with chaos game optimisation

Hand gesture recognition is considered an essential task in various human-computer interaction (HCI) applications. Therefore, developing a robust system for hand gesture recognition is a challenge. This work proposed a new hybrid approach named hybrid deep recurrent neural network (RNN) incorporated with a chaos game optimisation (CGO) algorithm for efficiently recognising hand gestures. The main objective of a hybrid recurrent neural network with chaos game optimisation (RNN-CGO) is to achieve the recognition of alphabet signs from 2D gesture images. The different stages included in this technique are pre-processing, feature extraction, feature selection, and classification. The proposed approach is executed using American Sign Language (ASL) dataset with the help of the PYTHON platform. The precision, accuracy, f1-score, and recall obtained for the proposed approach are 99.28%, 99.96%, 99.25%, and 99.28%, respectively. The inference time required for the proposed approach is 0.121 s. The proposed technique has less computational complexity. The results showed that the proposed approach is better than the existing hand gesture recognition model approaches.