Identification of Sign Language Alphabets on Static Images Using Deep Learning

Deaf and mute individuals often rely on sign language interpreters to facilitate communication. However, finding proficient interpreters can be challenging and costly. To address these challenges, a computerized interpreter could serve as a reliable and cost-effective alternative. Such a system would be capable of translating the sign language into plain text or audio in real-time, aiding in seamless communication. This paper integrates computer vision and employs deep learning techniques such as Convolutional Neural Networks (CNNs) to facilitate the real-time translation of sign language. These models quickly translate the user's sign into text by using computer vision to quickly record the user's sign. Through image processing, Convolutional Neural Networks facilitate the classification and prediction of various sign symbols. This integration of technology enables a seamless and instantaneous translation of sign language into textual form, promoting enhanced communication accessibility. By leveraging more amounts of data, deep learning algorithms aim to achieve high accuracy and generate predictions. The validation accuracy increased to 95.2% after 43 epochs and the validation loss dropped to 0.19. This demonstrates that the model was able to distinguish between Sign Language Recognition (SLR) and non-SLR photos with high accuracy and minimal error rates. The research intends to develop an automated system that can precisely recognize and translate sign language motions into written text by integrating deep learning and computer vision.