SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
This addresses license plate recognition in real-world conditions, but is incremental as it combines existing tasks into a joint framework.
The paper tackles license plate recognition from low-quality images by proposing SNIDER, an end-to-end network that jointly performs denoising and rectification, outperforming state-of-the-art methods on two challenging datasets.
In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rectification, and each task is conducted by Convolutional Neural Networks. Existing denoising and rectification have been treated separately as a single network in previous research. In contrast to the previous work, we here propose an end-to-end trainable network for image recovery, Single Noisy Image DEnoising and Rectification (SNIDER), which focuses on solving both the problems jointly. It overcomes those obstacles by designing a novel network to address the denoising and rectification jointly. Moreover, we propose a way to leverage optimization with the auxiliary tasks for multi-task fitting and novel training losses. Extensive experiments on two challenging LPR datasets demonstrate the effectiveness of our proposed method in recovering the high-quality license plate image from the low-quality one and show that the the proposed method outperforms other state-of-the-art methods.