Prediction of sign language recognition based on multi layered CNN

Sign Language Recognition (SLR) helps to bridge the gap between ordinary and hearing-impaired people. But various difficulties and challenges are faced by SLR system during real-time implementation. The major complexity associated with SLR is the inability to provide a consistent recognition process and it shows lesser recognition accuracy. To handle this issue, this research concentrates on adopting the finest classification approach to provide a feasible end-to-end system using deep learning approaches. This process transforms sign language into the voice for assisting the people to hear the sign language. The input is taken from the ROBITA Indian Sign Language Gesture Database and some essential pre-processing steps are done to avoid unnecessary artefacts. The proposed model is incorporated with the encoder Multi-Layer Convolutional Neural Networks (ML-CNN) for evaluating the scalability, accuracy of the end-to-end SLR. The encoder analyses the linear and non-linear features (higher level and lower level) to improve the quality of recognition. The simulation is carried out in a MATLAB environment where the performance of the ML-CNN model outperforms the existing approaches and establishes the trade-off. Some performance metrics like accuracy, precision, F-measure, recall, Matthews Correlation Coefficient (MCC), Mean Absolute Error (MAE) are evaluated to show the significance of the model. The prediction accuracy of the proposed ML-CNN with encoder is 87.5% in the ROBITA sign gesture dataset and it's increased by 1% and 3.5% over the BLSTM and HMM respectively.