Hand Sign Detector

Hand sign detection plays a crucial role in bridging communication gaps between hearing-impaired individuals and the general population. This paper presents a real-time Hand Sign Detection System using computer vision and deep learning techniques. The proposed system utilizes a webcam to capture hand gestures, processes the input using image preprocessing techniques, and classifies the gestures using a Convolutional Neural Network (CNN). The system is implemented using Python, OpenCV, and TensorFlow. Experimental results demonstrate that the model achieves high accuracy under controlled conditions. However, challenges such as lighting variation and background noise affect performance. The system provides a foundation for real-time sign language translation and human-computer interaction.