• Prof .Deveshree Wankhede
• Jaiswal Ashish
• Nandiskar Om
• Shettigar Siddhant

Hand gesture is one of the methods used in sign language for non-verbal communication. It is most commonly used by deaf & dumb people who have hearing or speech problems to communicate among themselves or with normal people. The proposed system would be a real time system wherein live sign gestures would be processed using image processing. Then classifiers would be used to differentiate various signs and the translated output would be displaying text. This project is designed to develop a machine learning model that can classify multiple identifiers used to fingerprint languages. In this model, the classification machine learning algorithm is trained on image data and tested on the data.The purpose of the system will be to develop a system prototype that will be able to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people.

Preprocessing of Dataset, Post-gaussian effect, Gaussian Blur Filter, Convolutional Neural Network (CNN) model

"Sign Language to Text Conversion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.137-142, July-2024, Available :http://www.jetir.org/papers/JETIRGK06018.pdf

"Sign Language to Text Conversion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pp137-142, July-2024, Available at : http://www.jetir.org/papers/JETIRGK06018.pdf