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Multilingual Conversion of Sign Language to Text

 

 

Sindhu M K1, Shraddha S J2, Vibha Gunaga3, Y G Sneha4

1Student, Dept. of Information Science Engineering, AMC Engineering College, Karnataka, India 2Student, Dept. of Information Science Engineering, AMC Engineering College, Karnataka, India 3Student, Dept. of Information Science Engineering, AMC Engineering College, Karnataka, India 4Student, Dept. of Information Science Engineering, AMC Engineering College, Karnataka, India

 

 

Abstract - This paper, Multilingual Conversion of Sign Language to Text aims to bridge the communication gap between the hearing-impaired community and non-sign language users through intelligent automation. Sign language, being a visual mode of communication, varies across regions, making translation across multiple languages a major challenge. This project utilizes computer vision and natural language processing (NLP) techniques to recognize and interpret hand gestures from sign language videos or live input. The captured gestures are pre-processed using background subtraction, image normalization, and frame extraction methods to enhance accuracy. A deep learning model, such as a Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM) network, is employed to classify gestures and translate them into corresponding text. The translated text is then converted into multiple natural languages using NLP-based translation models. The system is designed to support the Indian Sign Language (ISL), ensuring inclusivity and adaptability. This multilingual feature makes the system applicable in diverse regions and enhances accessibility in education, workplaces, and public communication. Overall, the proposed system offers an efficient, real-time, and scalable solution to foster inclusivity and promote equal communication opportunities for the hearing-impaired community.

 

Key Words: Hearing- impaired, Indian Sign Language, Hand gestures, NLP, LSTM, Multiple languages.

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