• Bushra Mujawar
• Shruti Wani
• Sakshi Kariya
• Anush Sharma
• Kishor Sawarkar

Although sign language is an essential tool for people who have hearing loss, there is still a communication gap between hearing and deaf people. This study suggests a machine learning and image processing-based sign language interpretation system. For people who are deaf or hard of hearing and those who are not skilled in using sign language, the system intends to deliver real-time translation of signs into text and speech. The suggested method classifies signs based on hand gestures taken by a camera using a convolutional neural network (CNN). The results show that the proposed system can translate sign language motions into meaningful words or sentences with a high degree of accuracy and usability. The system achieves an overall accuracy of 89% in classifying the signs of American Sign Language (ASL).

convolutional neural network (CNN), image processing, machine learning, sign language.

"Sign Language Interpreter using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.l658-l663, April-2023, Available :http://www.jetir.org/papers/JETIR2304B93.pdf

"Sign Language Interpreter using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppl658-l663, April-2023, Available at : http://www.jetir.org/papers/JETIR2304B93.pdf