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License plate recognition (LPR) is one of the essential components in intelligent transportation systems. Although the image processing algorithms for LPR have been extensively studied in the past several years, the recognition performance is still not satisfactory especially in unconstrained complex scenes. In order to tackle this issue, a novel deep multi-task learning-based method is proposed in this paper by introducing contextual information in multiple license plate frames. Specifically, an end-to-end trainable multi-task architecture, namely IQ-STAN, is developed by joint license plate recognition and image quality scoring. Moreover, we propose an image quality-guided spatio-temporal attention mechanism, which is utilized in the frame-level feature representation during the phase of plate recognition. Extensive experiments are conducted and the competitive results demonstrate the effectiveness of our …