Sign Language Translation into Text Using Deep Learning
摘要
Sign language is a crucial means of communication for the Deaf and hard of hearing communities. However, the gap between sign language and spoken/written language remains a significant barrier to accessibility. This paper presents a comprehensive overview of a deep learning approach for translating sign language into text, bridging the communication divide. We explore the application of deep neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), combined with sequence-to sequence models, to capture the nuanced and dynamic nature of sign language.