Sign language is essential for communication among individuals with hearing and speech impairments. However, most people do not understand sign language, creating a communication barrier. Automatic sign language recognition (SLR) can help by translating sign language into text or speech in real-time, making communication easier.

The goal of this research is to increase the accuracy and efficiency of SLR by applying deep learning techniques. The suggested approach makes use of Recurrent Neural Networks (RNNs), more especially Long Short-Term Memory (LSTM) networks, to comprehend the movement sequence and Convolutional Neural Networks (CNNs) to extract significant features from hand gestures. Transfer learning is also employed, in which performance is enhanced even with sparse data by using pre-trained models.

Additionally, hyperparameter tuning is used to modify the model for increased accuracy while cutting down on training time.

Experiments on popular sign language datasets demonstrate that the model outperforms conventional machine learning techniques. It operates quickly enough for real-time applications and recognizes intricate hand gestures with accuracy. Additionally, the model is made to be computationally efficient, which makes it appropriate for usage on embedded systems or smaller devices like smartphones.

This study helps create more scalable and accessible sign language interpreting solutions by refining deep learning-based SLR models. By using these developments in assistive technologies, people with speech and hearing impairments can interact with others more readily.