Sign Language Recognition Using OpenCV and Convolutional Neural Networks

Deaf and dumb people all over the world utilize sign language as a means of communication. Gesture detection is regarded as a challenging task in computer vision since it is complex for varied shapes. This research describes the application of Convolutional Neural Networks (CNN), an artificial intelligence technique, to recognize sign language gestures. The technique is a continuous selfie mode sign language video. The use of sign language with neural networks is superior to the gadget method because it is quicker and more precise. It provides a method for establishing two-way communication without the requirement of a translation. The HSV color technique is utilized to both render the background black and to identify hand gestures. The techniques used by the images for accuracy include grayscale, dilation, and mask operation. Regardless of skin tone, CNN utilizes filters of varying sizes to recognize the symbols. CNN is used to separate the photos. Using the CNN method, 10 American gesture alphabets and 20 Italian languages are recognized.