Enhancing Sign Language Recognition: A CNN-BiLSTM Approach for Accurate Gesture Interpretation

A crucial area of study and technology called Sign Language Recognition (SLR) seeks to reduce communication barriers for people whose first language is sign language. ways of getting in touch. It involves developing mechanisms that able to interpret and decode sign language motions, making it easier more inclusive and reachable interactions for the people of the hard of hearing and the deaf. Making use of a hybrid's power Bidirectional Long Short and Convolutional Neural Network, A new SLR called CNN-BiLSTM (CNN-Term Memory) model is shown in this research work. A remarkable accuracy of 98.6% is attained using the use of a dataset for Indian Sign Language, facilitating the effective recognition of gestures in sign language. In the framework of Indian Sign Language: This innovative method has a lot of promise. to enhance communication and accessibility for the hearing impaired and unable to hear others. It serves as a vital connection to further welcoming relationships and improved living standards.