SIGN-LANGUAGE TRANSLATOR USING MACHINE LEARNING

This research introduces a cutting-edge system designed to facilitate communication between individuals who are deaf and those who are hearing. By employing sophisticated machine learning algorithms and specific convolutional neural networks (CNNs), the proposed system can effectively sign language gestures into text or speech. A meticulously curated collection of sign language gestures serves as the foundation for training the model, ensuring its proficiency in accurately classifying several hand shapes and positions. To optimize classification performance, the system incorporates data preprocessing techniques that highlight the most distinctive features of the hands, thereby streamlining the computational process. This paper provides a comprehensive overview of the system's architecture, training methodology, and evaluation results, emphasizing the critical role of machine learning in developing inclusive communication tools that empower the deaf community. Future research initiatives will focus's on expanding the gesture dataset and refining real-time processing capabilities to further enhance the system's effectiveness and accessibility. Key Words: CNN, TensorFlow, Keras, NLTK