Research Article | Open Access
SIGN LANGUAGE IDENTIFICATION USING CNN
Mrs. A.ANITHA REDDY, Mareddy Sahani, Guddanti Meghana, Shaik Aslam Pasha, Tarun Madhav Medi
Pages: 840-845
Abstract
Sign language is a vital mode of communication for individuals with hearing impairments, facilitating their interaction with the broader society. Communication and accessibility for the deaf and hard of hearing community. In this study, we propose a Sign Language Identification (SLI) system based on Convolutional Neural Networks (CNNs) to recognize and interpret different sign gestures. We evaluate the SLI system on a separate test set, and the results indicate high accuracy and robustness in recognizing sign language gestures. The experimental outcomes demonstrate the potential of deep learning techniques, specifically CNNs, in developing accurate and efficient sign language identification systems. The proposed model could serve as a foundation for future research in sign language translation, interpretation, and accessibility applications.
Keywords
Sign language is a vital mode of communication for individuals with hearing impairments, facilitating their interaction with the broader society.