Recognition of American Sign Language Using Deep Convolution Network
Basel A. Dabwan, Mukti E. Jadhav · 2022
Around 466 million individuals are deaf or hard of hearing worldwide. Those individuals must engage with others, learn new things, and participate in activities. Sign language serves as a link between them and the rest of the world. There are numerous problems in creating a system that can automatically recognize sign language. For that, we have developed models for American Sign Language, we used a dataset that represents the American Sign Language Alphabet, which includes 29 classes for all English sign language letters as well as three additional characters: "del", "nothing" and "space." The collection contains 10,208 in image format, with each class presented in a separate folder. 80 percent of the dataset was used for training, while 20 percent was used for testing. The accuracy of the results for the proposed system was 100%.