Sign Language Translation to Natural Voice Output: A Machine Learning Perspective

According to statistics, 8% of the population cannot interpret sign language during conversations with deaf individuals. This creates a pressing need for sign language communication with many finding it challenging to communicate effectively without the assistance of an interpreter. The goal is to recognize a wide range of sign language variations and produce corresponding voice outputs facilitating seamless communication for deaf and hard-of-hearing people. This research work deals with the basic understanding of the problem statement and analyses the various techniques present in the existing era of sign language translation. Our objective is to recognize a diverse array of sign language variations and generate corresponding voice outputs in real-time, thereby fostering effective communication for the deaf and hard-of-hearing community. Through a comprehensive analysis of existing techniques in sign language translation, the necessity for advanced computational solutions capable of accommodating the intricacies of sign language gestures and expressions has been determined. The proposed methodology utilizes R-CNN, a state-of-the-art object detection framework, which is known for its accuracy in identifying objects present within images. By integrating R-CNN to recognize and interpret sign language gestures, this study intends to achieve robust performance across a spectrum of sign language variations. A significant aspect of this research study lies in its delineation of the computing environment requisite for implementing real-time sign language translation. By synthesizing theoretical frameworks with practical considerations, this study offers a comprehensive analysis for developing and deploying real-time sign language translation solutions. The proposed implementation has resulted in an accuracy of 92.25%. This research study significantly describes the computing environment and factors to be included to implement the same as a real-time application for translation and voice-based output.