Sign Language Detection System

Abstract: This research paper presents a Sign Language Detection System designed tobridge communication barriers for the hearing and speechimpaired.Byleveragingcomputervision techniques, the system utilizes OpenCV, MediaPipe,Scikit-learn, Numpy,andMatplotlib to detect and classify sign language gestures in real-time. The model is trained on an extensive dataset comprising various hand gestures to improve accuracy and responsiveness. Using Pickle for model serialization, the system achieves seamless loading and implementation, promoting accessibility and ease ofdeployment. This project demonstrates the impact of AI-driven solutions in assisting inclusivityandreducingcommunicationbarriers for differently-abled individuals.