Artificial Intelligence based Indian Sign Language Recognition with Accelerated Performance under HPC Environment

Abstract

Communicating with a person having a hearing or speech disability is always a major challenge. Sign Language (SL) is a medium to remove the barrier of such type of communication. It is a very tough task for a common man to understand SL and interprets its meaning. So, an automated system is necessary which can recognize the SL characters and display its meaning and semantics. In this context, this article has presented a systematic investigation of Artificial Intelligence (AI) based approaches towards examining the difficulties in the classification of characters in Indian Sign Language (ISL). In this work, we adapted ISL recognition using Computer Vision, Machine Learning and Deep Learning methodologies. To achieve this requirement, the captured image undergoes a series of pre-processing steps which include various Computer Vision techniques such as conversion to gray-scale and thresholding using OTSU algorithm. Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and pre-trained models, VGG-19 and Inception-V3using Transfer Learning mechanism are used to train the system. Further, due to large image dataset, the training time of the models are also accelerated using PARAM SHAVAK HPC system which shows a reasonable improvement in the performance of the models.