Abstract:
Key Effective communication between deaf and hearing individuals can be challenging
due to the lack of efficient sign-language recognition systems. To address this
challenge, a Deep Learning-Based Approach for Sign Language Detection using a
Convolutional Neural Network (CNN) is proposed. The model is trained and evaluated
on a standard sign language image dataset consisting of 7500 images belonging to 25
classes, with each class having 300 images. The dataset is split into training and testing
data in a ratio of 80:20, respectively, by randomly selecting images from the dataset.
The proposed approach achieves a remarkable accuracy of 94% in detecting sign
language gestures in real time. Machine learning algorithms through the image
classification method based on the CNN model and libraries such as TensorFlow,
Keras, and OpenCV with Python are used to develop the deep learning-based approach
for sign language detection. The video frame is labeled according to the sign language
gesture being performed by the person. The results of the research demonstrate the
effectiveness of deep learning-based approaches for sign language detection and
contribute to the development of more advanced and efficient sign language recognition
systems. This technology has the potential to significantly improve communication and
interactions between deaf and hearing individuals.
