Herein, a deep learning-based doorplate detection method was proposed to realize the global positioning of mobile robots in indoor environments. First, the target detection algorithm based on MobileNet-SSD was used to detect the doorplate area of the image acquired using a monocular camera. Second, an improved rotating projection method was proposed to correct oblique images. Third, doorplate number recognition was performed using the k-nearest neighbor algorithm. Fourth, the speeded-up robust feature point matching was performed based on the precollected front-view template pictures of each doorplate. Then, the camera pose solution was achieved based on the PnP problem. Finally, the global positioning of the mobile robot was realized according to the coordinate transformation. Experiments using a mobile robot in an office environment show that the average position error of the mobile robot based on the proposed method is about 7 cm. Moreover, the orientation error is 0.1712 rad, which is reduced by about 50% compared with using only the adaptive Monte Carlo method, and the angle error is reduced by about 57%.