Efficient Indian sign language recognition and classification using enhanced machine learning approach

Deafness and voice impairment are two significant disabilities that make it difficult for people to communicate in verbal languages with others in a verbally communicating population. To solve this problem, the sign language recognition (SLR) system was constructed by combining machine learning and deep learning. The SLR employs hand gestures to convey messages. Earlier research aims to develop vision-based recognisers by extracting feature descriptors from gesture photos. When dealing with a large sign vocabulary recorded under chaotic and complex backgrounds, these strategies are ineffective. Hence, an improved convolution neural network is proposed in this paper to predict the most frequently used gestures in the Indian population with improved efficiency. The presented system is compared to SVM and CNN. The suggested approach is tested on 2,565 UCI instances and 22 training attributes. It showed both-handed ISL movements against various backgrounds. The augmented CNN has a precision of 89% and 90.1% accuracy, which is higher than most other approaches. According to this survey, we had an 83% recall and a 0.4 F score. Python evaluates our work.