Indian Sign Language Recognition Using Surf Feature Extraction and MDAE for Patient Disability Discussion

A hand sign for a disabled patient is a useful tool for human-to-human communication that can be used for a variety of purposes. They are frequently utilised by speech-impaired people around the world for communication since they are a natural form of engagement. In actuality, this group makes up around 1% of the Indian population. To enable easy communication among the signer and non-signer communities, an effective sign language recognition system (SLRS) can identify sign language motions. This research proposes a deep learning-based SLRS for classification that is computer vision-based. A sample of Auslan (Australian Sign Language (ASL)) signs is included in the dataset. Using top-notch position trackers, 27 examples of each of the 95 Auslan signs were recorded by a native signer. However, films captured Indian Sign Language (ISL) was filmed utilising numerous signs. Three individuals submitted 26 letters (A–Z) and 10 numbers (0–9). Few background removal techniques were performed on the samples and various skin color segmentation process was done. Third, a brand-new and reliable model for classifying ISL motions has been suggested utilising the mutation denoising autoencoder (MDAE). ISL and ASL datasets are publicly available and have been used to measure the effectiveness of the proposed system. In terms of measures like precision, recall, F1-Score, and accuracy, the proposed system is contrasted with well-known computer vision algorithms like the Support Vector Machine (SVM), Convolution Neural Network (CNN), Enhanced Convolution Neural Network (ECNN), and DAE. Python has been used to implement and test these categorisation techniques.KeywordsHand sign recognitionBag of visual words (BOVW)Speeded Up Robust Features (SURF)Mutation Denoising Autoencoder (MDAE)Support Vector Machine (SVM)Convolution Neural Network (CNN)Indian and Australian sign languagesdisability in patient discussionphysical state