AI powered sign language detection with voice output using deep learning

Communication barriers faced by individuals with speech and hearing impairments often hinder their interaction with the wider community. While sign language serves as an essential medium for expression, its limited understanding among the general population restricts inclusivity. This project proposes a real-time sign language detection system with voice output to bridge this communication gap. Leveraging computer vision and deep learning techniques, the system recognizes hand gestures corresponding to sign language and translates them into text, which is then converted into audible speech using a text-to-speech (TTS) engine. Convolutional Neural Networks (CNNs) are employed for static gesture recognition, while a hybrid CNN-LSTM architecture handles dynamic gestures effectively. Advanced methods, including MediaPipe-based 3D hand pose estimation, are integrated to enhance accuracy and robustness in real-world environments. The system supports deployment on mobile devices using TensorFlow Lite, ensuring portability and accessibility. With its high accuracy, real-time processing, and seamless gesture-to-speech translation, the proposed solution demonstrates significant potential as an assistive technology for educational tools, healthcare, customer service, and smart devices, fostering inclusivity and accessibility in daily interactions.