Sign Language is the most expressive way for communication between deaf and hearing-impaired people, where information is majorly conveyed through the hand gestures. Despite this, one of the most issues that facing people and public domains that are outside the deaf community is understanding sign language, they are need an interpreter to understand this language. Technologies including deep learning and computer vision aid this issue by providing several technical apps and other platforms. In this paper, we propose a real-time sign language detection model using TensorFlow and OpenCV. The main aim of this study is developing model that recognize sign language, and that can be used offline. Through this model, we can detect a common words and sentences that are expressed by hand gestures, like among American and Arabic sign languages like good (thumbs up), bad (thumbs down), thanks, and live long. This study is belonging to the experimental and applicable research. The results of this study were that the model succeed to detect the hand gestures and recognize it with accuracy 100 %.