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

arXiv: https://arxiv.org/abs/2408.15103 · HTML: https://mlsanity.com/paper/2408.15103/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Blind Image Super-Resolution: A Survey and Beyond](https://mlsanity.com/paper/2107.03055/) (2021)
- [MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution](https://mlsanity.com/paper/2011.04566/) (2020)
- [An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector](https://mlsanity.com/paper/1909.01754/) (2019)
- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [Rethinking and Designing a High-performing Automatic License Plate Recognition Approach](https://mlsanity.com/paper/2011.14936/) (2020)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks](https://mlsanity.com/paper/1911.05541/) (2019)
- [Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution](https://mlsanity.com/paper/2210.16836/) (2022)
- [Face Super-Resolution Using Stochastic Differential Equations](https://mlsanity.com/paper/2209.12064/) (2022)
- [Leveraging Model Fusion for Improved License Plate Recognition](https://mlsanity.com/paper/2309.04331/) (2023)
- [Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers](https://mlsanity.com/paper/2305.17313/) (2023)
- [Forensic License Plate Recognition with Compression-Informed Transformers](https://mlsanity.com/paper/2207.14686/) (2022)
- [Ambiguity of Objective Image Quality Metrics: A New Methodology for Performance Evaluation](https://mlsanity.com/paper/2101.07439/) (2021)

## Built on by (most cited first, 8 of 8)

- [Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark](https://mlsanity.com/paper/2505.06393/) (2025)
- [LPLC: A Dataset for License Plate Legibility Classification](https://mlsanity.com/paper/2508.18425/) (2025)
- [Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition](https://mlsanity.com/paper/2604.05271/) (2026)
- [LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification](https://mlsanity.com/paper/2604.08741/) (2026)
- [ICPR 2026 Competition on Low-Resolution License Plate Recognition](https://mlsanity.com/paper/2604.22506/) (2026)
- [Revisiting Vehicle Color Recognition in Long-Tailed Surveillance Scenarios](https://mlsanity.com/paper/2606.13625/) (2026)
- [Robust Face Super-Resolution and Recognition Through Multi-Feature Aggregation in Diffusion Models](https://mlsanity.com/paper/2607.05702/) (2026)
- [CharDiff-LP: A Diffusion Model with Character-Level Guidance for License Plate Image Restoration](https://mlsanity.com/paper/2510.17330/) (2025)

## Abstract (arXiv)

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