Sign Language to Text at Real Time

Author(s)	Mr. Avinash Raghunath Sonule, Ms. Sanika Shirsat, Ms. Anamika Shirke, Ms. Niharika Dasari, Mr. Vikas Mendhe
Country	India
Abstract	For the communities of people who are deaf or hard of hearing, sign language is an essential form of communication that offers a wealth of opportunities for engagement and expression. However, when people who are not competent in sign language try to communicate with others who are, difficulties frequently occur. The necessity for creative solutions that promote smooth communication across various cultures is highlighted by this disparity. This paper is aimed at developing a Neural Network using LSTM and Dense layers to translate any Sign Language into Text. It provides a user-friendly way for individuals to train their own Neural Network model and enables real-time predictions as well as grammar correction of predicted sentences. It is recommended that the user records a substantial number of sequences changing the position of their hands. This way the user can ensure data diversity which helps to obtain a generalized model.
Keywords	Neural Network, Long Short-Term Memory (LSTM), Dense layers
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-23
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58690

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