Sign Language Classification Text And Voice Output System Using Resnet

Abstract

Sign Language Is A Crucial Communication Medium For Individuals With Hearing And Speech Impairments. However, The Lack Of Widespread Accessibility To Sign Language Interpreter’s Limits Communication Opportunities For The Deaf And Mute Community. This Project Presents A Sign Language Classification And Voice Output System Using ResNet, A Deep Learning-based Model Designed For Accurate Sign Language Recognition. The System Processes Images And Video Frames Of Hand Gestures, Classifies Them Into Meaningful Words Or Letters, And Converts Them Into Speech Output. By Leveraging Convolutional Neural Networks (CNNs) With ResNet Architecture, This System Improves Recognition Accuracy And Real-time Responsiveness. The Model Is Trained Using Benchmark Sign Language Datasets And Optimized With Image Pre-processing Techniques. Performance Evaluation Is Carried Out Using Standard Metrics Such As Accuracy, Precision, Recall, And F1-score. This Study Demonstrates How Deep Learning Can Bridge The Communication Gap For Hearing-impaired Individuals, Providing An Effective Real-time Sign Language Recognition System.

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