Hand Talk: Sign Language to Text Converter using CNN

This research presents a novel system for converting sign language gestures into text, bridging the communication gap between non-sign language speakers and the deaf and dumb community. The system utilizes advanced image processing and machine learning techniques to accurately interpret and translate sign language. The proposed system employs a Convolutional Neural Network (CNN) classifier to process captured hand gestures and convert them into text. Through rigorous training and testing, the SVM classifier achieved a 96.7% accuracy rate for one-minute intervals. This research contributes to the development of accessible and efficient sign language interpretation systems, facilitating communication and promoting inclusivity. By bridging the language barrier, this technology can enhance social interactions and improve the quality of life for individuals with hearing impairments.