Arabic Sign Language Detection Using Deep Learning

Deaf or hard of hearing person number roughly 466 million people globally. Individuals in this situation must interact with people, gain new skills, and participate in activities. They use sign language to communicate with the rest of the world. The development of an autonomous system to detect gesture language has various difficulties, particularly for Arabic sign language. We built models for Arabic Sign Language to accomplish this. We used the Arabic Alphabets Sign Language (ArSL) Dataset, which contains 32 categories of sign characters, A total of 40 people of various ages participated in these Arabic sign and letter classes. There are 506 images in each category, for a total of 506 * 32 = 16192 images. 80 percent of the dataset was used for training, while 20 percent was used for testing. The results of the proposed system were 100 percent accurate.