Vehicle license platerecognition in natural scene is an important research topic in computer vision. The license plate recognition approach in the specific scene has become a relatively mature technology. However, license plate recognition in the natural scene is still a challenge since the image parameters are highly affected by the complicated environment. For the purpose of improving the performance of license plate recognition in natural scene, we proposed a solution to recognize real-world Chinese license plate photographs using the DCNN-RNN model. With the implementation of DCNN, the license plate is located and the features of the license plate are extracted after the correction process. Finally, an RNN model is performed to decode the deep features to characters without character segmentation. Our state-of-the-art system results in the accuracy and recall of 92.32 and 91.89% on the car accident scene dataset collected in the natural scene, and 92.88 and 92.09% on Caltech Cars 1999 dataset.