Natural scene text detection and recognition is important for obtaining information from scenes, and it can be improved by the help of deep learning. In this study, the deep learning-based methods of text detection and recognition in natural scenes are classified, analyzed, and summarized. Firstly, the research background of natural scene text detection and recognition and the main technical research routes are discussed. Then, according to different processing phases of natural scene text information processing, the text detection model, text recognition model and end-to-end text recognition model are further introduced, in which the basic ideas, advantages, and disadvantages of each method are also discussed and analyzed. Furthermore, the common standard datasets and performance evaluation indicators and functions are enumerated, and the experimental results of different models are compared and analyzed. Finally, the challenge and development trends of deep learning-based text detection and recognition in natural scenes are summarized.