DEEP LEARNING BASED SOUTH INDIAN SIGN LANGUAGE RECOGNITION BY STACKED AUTOENCODER MODEL AND ENSEMBLE CLASSIFIER ON STILL IMAGES AND VIDEOS

Recently, sign or gesture recognition has been challenged by concerns like high computational cost, occlusion of hands, and inaccurate tracking of hand signs and gestures. The existing models face difficulty in managing longer term sequential data, due to poor information learning and processing. To highlight the aforementioned concerns, a novel deep learning based ensemble model is proposed in this article. Firstly, the sign/gesture images are acquired from American Sign Language (ASL)-Modified National Institute of Standard and Technology (MNIST) and real time South Indian Sign Language (SISL) databases. In addition, K-means clustering with the Gaussian blur method is implemented for precisely segmenting the sign/gesture region. Next, the feature extraction is carried-out using Gray-level Co-occurrence Matrix (GLCM) features and AlexNet, and then the dimensionality of the extracted feature vectors are decreased using a deep learning model: stacked autoencoder. The dimensionally decreased feature vectors are fed to the ensemble classifier (Multi-Support Vector Machine (MSVM) and Naive Bayes) to classify 24 alphabets and 30 SISL classes on the ASL-MNIST and real time SISL databases. The extensive experiments demonstrated that the ensemble based stacked autoencoder model achieved 99.96% and 99.08% of accuracy on the ASL-MNIST and real time SISL databases, which are better related to the traditional machine learning classifiers.