Authors : Dr. Bhuvaneshwari K V; Bindu A R; Manvitha G K; Nikitha N Chinchali; Nisha K N

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/yeyxv5s7

Scribd : https://tinyurl.com/493wu8ww

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY273

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Abstract : Communication is very important in human daily life and the most widely used type of communication is verbal communication. But there are people with hearing and speech impairment who cannot communicate verbally and the language which they use for communication is sign language. Many other languages, tools are being developed for inter-language translation from sign language to text. There has been a lot of research done in the field of American Sign Language but the work is limited in the case of Indian Sign Language. This is due to lack of standards and the variation in the language. The proposed system aims to recognize Indian Sign Language digit gestures and convert it into text. By using Machine Learning Techniques, sign language recognition leads to the development of a more accurate and robust system. As Deep learning techniques, ResNet100 and ensemble models continue to evolve, sign language recognition system plays a transformative role in bridging the communication gap between deaf and hearing individuals. It helps the user to recognize the sign language by using this proposed system.

Keywords : Sign Language, Convolutional Neural Networks, Residual Network , Random Forest Classifier, Ensemble Model.

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