Enhanced Accessibility for Recognizing Indian and American Sign Language through Deep Learning Techniques

Sign language detection, specifically for Indian Sign Language (ISL) and American Sign Language (ASL), plays a crucial role in bridging communication gaps between hearing impaired and normal people..This paper proposes a systematic methodology using different deep learning models to detect ISL and ASL from an image dataset of sign language gestures. The algorithms probed were 2D Convolutional Neural Networks (CNN), ResNet-50, Long Short Term Memory (LSTM) and VGG16. By using a dataset we conducted an extensive comparative analysis of these algorithms. The results portrayed LSTM as the best predictive classifier with an amazing 97.97% accuracy. This excellent result makes LSTM a strong model for the classification of sign languages with high accuracy. The target classes involved several signs from ISL and ASL are thus extensive for recognizing and interpreting both these languages by using a number of algorithms we make the predictive model more robust as it can handle multiple kinds of data patterns corresponding to different sign gestures. The outcomes of our investigation present the very first step towards an intelligent sign language detection system. A well-recognized and fast-emerging potential of deep learning is in the automatic detection of sign languages, which introduces a drastic positive change in communication efficiency as this research field grows exponentially. This could greatly improve the ability of a person to interact and the accessibility of an individual to sign languages.