Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach

TL;DR

This paper introduces a novel super-resolution method for license plates that incorporates layout and character awareness, using a new loss function, deformable convolutions, and GAN training with OCR guidance, significantly improving character reconstruction in low-res images.

Contribution

The paper proposes a layout-aware and character-driven super-resolution approach with a new loss function and GAN training guided by OCR, addressing real-world low-resolution license plate challenges.

Findings

Outperforms state-of-the-art methods in character reconstruction quality.

Significant improvements in both quantitative and qualitative measures.

Effective in low-resolution, blurry license plate scenarios.

Abstract

Despite significant advancements in License Plate Recognition (LPR) through deep learning, most improvements rely on high-resolution images with clear characters. This scenario does not reflect real-world conditions where traffic surveillance often captures low-resolution and blurry images. Under these conditions, characters tend to blend with the background or neighboring characters, making accurate LPR challenging. To address this issue, we introduce a novel loss function, Layout and Character Oriented Focal Loss (LCOFL), which considers factors such as resolution, texture, and structural details, as well as the performance of the LPR task itself. We enhance character feature learning using deformable convolutions and shared weights in an attention module and employ a GAN-based training approach with an Optical Character Recognition (OCR) model as the discriminator to guide the super-resolution process. Our experimental results show significant improvements in character reconstruction quality, outperforming two state-of-the-art methods in both quantitative and qualitative measures. Our code is publicly available at https://github.com/valfride/lpsr-lacd