| Title | Advancements in Sign Language Recognition through Deep Learning: Techniques, Challenges, and Applications |
| Research Area | Computer Science Engineering |
| Abstract | Sign language serves as a vital communication tool for millions of individuals worldwide, especially within the deaf and hard-of-hearing community. However, its integration into digital platforms remains limited. Recent advancements in deep learning techniques have significantly improved the ability to recognize sign language gestures, facilitating better interaction between sign language users and digital systems. This paper explores the use of deep learning methodologies in sign language recognition, evaluating current techniques, challenges, and future prospects. Through a comprehensive review of existing literature and current systems, this paper provides insights into the effectiveness of deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), and other machine learning models in achieving high accuracy in sign language recognition. Additionally, it discusses the potential applications of this technology in real-time communication, accessibility, and human-computer interaction. |
| Keywords | Sign Language |
| Paper Status | Published |
| Volume | 2 |
| Issue | 2 |
| Published On | 05/09/2025 |
| Published File |
IJSRTD_4835.pdf
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