Real-Time Sign Language Recognition and Translation Using CNN and BiLSTM for Enhanced Communication Accessibility

The people with speech and hearing impairments, communication obstacles pose a serious obstacle to promoting accessibility and equality. Using cutting-edge computer vision and machine learning techniques, this study presents a real-time sign language recognition system intended to close this gap. Three essential features are included in the system. In the first place, it makes it possible to identify sign language gestures in real time, precisely anticipating and classifying signs as they are made. This guarantees instant understanding and promotes smooth communication. A function for sign detection from videos is also included in the system, enabling users to submit previously recorded material for gesture analysis and translation. This increases the system's usefulness in a variety of settings and forms. Through a Sign Language Emoji Translation module, the study also investigates a novel method of communication. Through the use of distinct emojis for each letter of the alphabet, this feature allows people to communicate visually. This method makes nonverbal communication easier to understand and more interesting, especially for people who have speech or hearing problems. The system provides a basis for future improvements, guaranteeing that it can adapt to a variety of real-world requirements because it was designed with scalability and adaptability in mind. This project attempts to contribute to the creation of an inclusive, user-friendly communication tool that bridges the gap between people with diverse communication capacities by combining real-time recognition, video analysis, and emoji-based translations.