Sign Language Recognition Using Machine Learning

The community of the deaf and hearing-impaired uses sign language as the primary but not exclusively dominant mode of communication. A strong system that can translate spoken language into sign language and vice versa must be built for the hearing and hearing-impaired groups to communicate easily and mutually. Currently, a sensor-based approach utilizing glove sensors and built-in sensors, along with CNN (Convolutional Neural Network) algorithms for image processing, is employed for recognizing sign language gestures. The proposed method for recognizing sign language is using LSTM (Long Short-Term Memory), a kind of RNN that can recognize long-term dependencies, is used in the development. Apart from single data points like photos, which can process the full data stream The system can be utilized for machine translation and speech recognition, among other things. The suggested system offers a substantial contribution in that it uses advanced technology to facilitate communication between the hearing and hearing-impaired groups. The system provides precise gesture identification in sign language by LSTM algorithms, removing obstacles to efficient communication. By achieving accuracy levels between 90 and 100%, the system can effectively comprehend the complex nature of sign language by utilizing LSTM algorithms to capture long-term dependencies in sequential data.