Sign Language Detection

“Sign Language Detection” encapsulates the process of communication, facilitating the exchange of information, ideas, and emotions through a shared language for mutual understanding. However, when it comes to individuals who are deaf, hard of hearing, or mute, their method of communication differs. In these cases, sign language becomes the primary mode of communication, empowering them to communicate within their own community and with the broader society. Sign language relies on the intricate use of hand gestures, facial expressions, and body language to convey meaning and emotions. Our project employs advanced Machine Learning Techniques, in conjunction with the TensorFlow Object Detection API, to create an efficient real-time sign language detection system. The TensorFlow Object Detection API serves as the foundational framework for training and deploying our model, with the integration of OpenCV to ensure precise and effective sign language recognition. Furthermore, we utilize the Protocol Buffers library for streamlined data management, while the LabelImg annotation tool plays a crucial role in creating high-quality training datasets, meticulously labelling images with bounding boxes around sign language gestures.