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In this paper, we present an algorithm for real-world license platerecognition (LPR) from a low-quality image. Our method is built upon aframework that includes denoising and rectification, and each task is conductedby Convolutional Neural Networks. Existing denoising and rectification havebeen treated separately as a single network in previous research. In contrastto the previous work, we here propose an end-to-end trainable network for imagerecovery, Single Noisy Image DEnoising and Rectification (SNIDER), whichfocuses on solving both the problems jointly. It overcomes those obstacles bydesigning a novel network to address the denoising and rectification jointly.Moreover, we propose a way to leverage optimization with the auxiliary tasksfor multi-task fitting and novel training losses. Extensive experiments on twochallenging LPR datasets demonstrate the effectiveness of our proposed methodin recovering the high-quality license plate image from the low-quality one andshow that the the proposed method outperforms other state-of-the-art methods.

SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition