License plate detection has an important role to play in intelligent transportation systems. It aims at locating the license plate and accurate region information in the image. In recent years, there are lots of efficient detection methods based on the application of deep convolutional networks proposed. Although these approaches have presented promising results, these methods use horizontal or nearly horizontal bounding boxes to locate the object which is inappropriate for multi- oriented object in the real world. In this paper, a new license plate detection framework has been proposed for oriented license plate detection which combines the oriented region with a deep learning method to recognize oriented license plate detection. The proposed framework contains three sub- networks for extracting, generating and prediction proposals. Experimental results for license plate detection from the Chinese City Parking Dataset demonstrate that the proposed approach has good performance under specific scenarios.