Convert sign language to text with CNN

Effective communication is crucial in our daily lives, and it occurs through various channels such as vocal, written, and body language. However, individuals with hearing impairments often rely on sign language as the primary means of communication. The inability to understand sign language can lead to isolation and barriers in communication, hindering the social lives of deaf individuals. To address this need, we propose a marker-free, visual Indian Sign Language identification system that employs image processing, computer vision, and neural network techniques. Our proposed system analyzes video footage captured by a webcam to recognize hand gestures and translate them into text, which is subsequently converted into audio. The system uses a range of image processing techniques to identify the shape of the hand from continuous video frames, including background subtraction, thresholding, and contour detection. The Haar Cascade Classifier algorithm is used to interpret the signs and assign meaning to them based on the recognized patterns. Finally, a speech synthesizer is employed to convert the displayed text into speech. The proposed system is intended to improve the social lives of deaf individuals by facilitating communication with hearing individuals. It is designed to be user-friendly, efficient, and affordable, as it does not require any additional hardware or markers to recognize signs. The proposed system could be integrated into various devices such as smartphones, tablets, or laptops, making it accessible to a wide range of users. The implementation of such a system could potentially break down communication barriers between the deaf and hearing communities, providing deaf individuals with more opportunities to interact with others and participate in society.