Sign-Speak: Real-Time Continuous Indian Sign Language Translator

Abstract

Communication between deaf or hard-of-hearing individuals and people who are unfamiliar with Indian Sign Language (ISL) continues to present challenges in many everyday situations. Recent progress in artificial intelligence has made it possible to develop camera-based systems that recognize sign gestures without relying on wearable devices. This literature survey examines recent research on real-time ISL recognition, with particular attention to landmark-based feature extraction and sequence-learning models used for dynamic gesture interpretation. Four representative studies employing MediaPipe Holistic, Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Transformer-based techniques are critically reviewed. Their methodologies, datasets, reported performance, strengths, and limitations are compared to identify current research trends and unresolved challenges. The analysis reveals that although existing approaches achieve promising recognition accuracy, many are constrained by limited vocabularies, small datasets, signer variability, and insufficient support for continuous sentence-level recognition. Based on these observations, this survey proposes a Sign-Speak framework that combines MediaPipe Holistic for extracting hand, facial, and body landmarks with a Bi-LSTM network for learning temporal gesture patterns. The recognized signs are intended to be converted into meaningful text and speech, enabling more accessible communication in real-world environments. The findings highlight the potential of vision-based deep learning systems to improve inclusive human-computer interaction while identifying opportunities for future research in multilingual translation, larger-scale datasets, and practical deployment.