• Mallavarapu Abhilash
• Lingavajhula Sai Preetam
• Loka Sathvika Reddy
• Muttireddy Mithil Varsh Reddy

This research presents a hand sign recognition system based on Convolutional Neural Networks (CNNs) for numerical digits. The system achieves high recognition accuracy, robustness, and computational efficiency by leveraging CNN architecture and appropriate pre-processing techniques. Experimental results demonstrate the system's effectiveness in accurately classifying hand signs, even under challenging conditions such as variations in lighting, hand pose, and background clutter. The proposed system holds significant potential for applications in communication accessibility and assistive technologies, bridging the communication gap for individuals with hearing impairments. Future work can focus on expanding the system's capabilities for dynamic hand signs, multi-modal integration, and fine grained recognition, to further enhance its usability and impact.

deep learning, hand sign recognition, cnn

"Hand Sign Recognition Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.a494-a497, July-2023, Available :http://www.jetir.org/papers/JETIR2307061.pdf

"Hand Sign Recognition Using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppa494-a497, July-2023, Available at : http://www.jetir.org/papers/JETIR2307061.pdf