Multiple surveillance cameras provide huge video resources that need further mining to collect traffic stream data such as license plate recognition (LPR). However, these surveillance cameras have limited spatial resolution, which may not always suffice to precisely recognize license plates by existing LPR systems. This work is focused on the LPR method in low-quality images from surveillance video screenshots on urban road. The methodology we proposed is based on vehicle tracking and result integration, and we recognize the plate with an end-to-end method without character segmentation. First, we track each vehicle to get vehicle tracking sequence. Moreover, a plate detector based on an object detection framework is trained to detect license plates of each vehicle from the sequence and a license plate sequence is formed. In addition, an end-to-end convolutional neural network architecture is applied to recognize license plates from the sequence. Finally, we integrate the recognition result of continuous frames to get the final result. Evaluation results on multiple datasets show that our method significantly outperforms others without segmentation or integration in real traffic scene.