Deep kullanılarak el işaret dilinin sınıflandırılması öğrenme

People with hearing disabilities face many problems, which impede many of their social life issues in all areas of communication, so effective communication is crucial in the development of a nation. It promotes understanding and inclusivity among all members of the community, including those who are deaf. Good communication is key to building and maintaining a strong, cohesive society. I used 29,000 sign language images, each class contained 1,000 images. I built a model from scratch (ASL.model) and compared it with pre-existing models (Xception, Inception, ResNet, VGG16 and MobileNet). The study of intelligent computers that can carry out activities without direct human guidance is known as artificial intelligence (AI), a fast-growing topic within computer science. These tasks may include learning, decision making, and problem solving, and they are often accomplished through the use of algorithms, data, and machine learning techniques. To find the most appropriate classification features to be used for classification, deep learning techniques will be employed in this thesis to create a model for classifying sign language utilizing photographs obtained from the Kaggle depository as a training data set. Deep learning is now widely employed across a variety of industries due to its accuracy and efficiency, particularly for vast yet complicated data, such as photos, sounds, or text, where deep learning algorithms are taught using massive, labeled data sets. In this thesis, we used deep learning to classify a total of twenty-nine sign language-related classes. We put forth a fresh framework for categorizing sign language. The suggested model was put into practice, trained, verified, and tested. The model passed the test with a 99.97% success rate. To assess the effectiveness of our suggested model (ASL.model) with that of these methods, we also employed five pre-trained models (Xception, Inception, ResNet, VGG16, and MobileNet) of assisting the performance of deep learning algorithms that use Convolutional Neural Networks (CNNs). The five pre-trained models had F1-score levels of 100%, 99.51%, 99.87%, 100%, and 99.68%, respectively. The model Xception and VGG16 outperformed all others in terms of testing accuracy but when the testing time was smaller than 1.45 seconds, the suggested model outperformed all others in terms of time.