Sign language is a language that uses hand gestures and facial expressions to communicate. Sign language consists of either static or dynamic gestures. Sign language is the main and only communication tool for the deaf and hard of hearing. Therefore, the deaf cannot interact with non-deaf people without a sign language interpreter. Accordingly, sign language recognition automation has become an important application in artificial intelligence and deep learning. Specifically, the recognition of Arabic sign language has been studied using many smart and traditional methods. However, there is still no published study to recognize the Saudi Sign Language based on Saudi sign language dictionary. This research provides a system to rec- ognize dynamic Saudi sign language based on real-time videos to solve this problem. We will construct a dataset in the proposed system and then build a model using the Convolutional long short-term memory (convLSTM) to recognize dynamic signs. Implementing such a system provides a platform for deaf people to interact with the rest of the world without an interpreter to reduce deaf isolation in society.