• Pranay Bante
• Sarika Kamble
• Nitikesh P.Raut
• Shrinath L.Kashid
• S.S.Bhosale, C. G. Patil

The only way speech and hearing-impaired people can communicate is by using the sign language. The main problem with this kind of communication is that the non-impaired people, who cannot understand the sign language, would not be able to communicate with these people or vice versa. The project is intentionally designed to allow deaf and dumb communities to convey messages and connect with the society. It aims to bridge the gap between speech and hearing impaired people and the non-impaired people. Speech impairment is a disability that affects an individual’s ability to communicate effectively. Many existing studies have proposed methods for sign language recognition The project uses a machine learning system to identify, especially English alphabetic sign language, and convert it into text. The focus of this work is to create a vision based application that offers American Sign Language translation to text thus aiding communication between signers and non-signers. The proposed model takes image sequences and extracts temporal and spatial features from them.

Machine learning Alphabet Detection, Sign recognition, Text-to-speech algorithms.

"A Comparative Approach an Alphabet Detection in Sign Language Using Machine Learning Algorithm”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.470-476, August-2022, Available :http://www.jetir.org/papers/JETIRFP06083.pdf

"A Comparative Approach an Alphabet Detection in Sign Language Using Machine Learning Algorithm”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp470-476, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06083.pdf