A Review on Indian Language Identification Using Deep Learning

Indian Language Identification and classification has several practical applications and has received a lot of interest in the computer vision research field over the last few decades and its significance is boosted by its applications in deep learning. Despite the fact that various language identification and classification techniques in deep learning have been presented previously, there is no research that focuses on reviewing hybrid features and attention from various languages along with contrasting simple architecture from multiple architecture. Hence, this review provides an overview of various strategies for Indian language identification and classification using hybrid extraction, hybrid deep learning with also considered hybrid features from simple architecture to hybrid architecture. Finally, an effective comparison analysis has been made based on the performance analysis of various simple and hybrid language identification techniques. The state-of-art research relevant to each aspect of the language identification and classification is presented which evaluates their strength, weakness, and overall applicability for deep learning applications. Finally, concluding remarks based on the drawbacks and its respective solutions has been provided.