Sign language Recognition Using Deep Learning Algorithm

This research focuses on developing a sign language recognition system utilizing deep learning algorithms to enhance communication accessibility for the deaf and hard-of-hearing community. Using a Convolutional Neural Network (CNN) architecture, we classify sign language gestures captured through a webcam. Our approach involves training the CNN with a dataset comprising 5-10 distinct sign language classifications, including gestures like 'hello,' 'OK,' 'bye,' 'you,' 'what,' and 'house.' The CNN model is constructed with multiple layers to efficiently learn spatial hierarchies and intricate patterns from the input images. The training process incorporates various techniques to optimize performance, such as data augmentation to increase dataset variability and prevent overfitting, and hyperparameter tuning to improve accuracy. The webcam continuously captures real-time video frames, which are preprocessed and fed into the CNN model for classification. Our preliminary results demonstrate that the model can accurately recognize and differentiate between the specified sign language gestures, showcasing the potential of deep learning in facilitating effective and real-time sign language communication. Future work will aim to expand the dataset and refine the model for more comprehensive sign language recognition.

Keywords: Sign language, deep learning, CNN, sign language dataset.