Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark

TL;DR

This paper introduces a new real-world license plate super-resolution dataset and benchmark, demonstrating that super-resolution techniques significantly improve license plate recognition accuracy in challenging conditions.

Contribution

The paper provides a novel dataset, UFPR-SR-Plates, and establishes a benchmark with multiple models and fusion strategies for license plate super-resolution in real-world scenarios.

Findings

Super-resolution increases license plate recognition accuracy from 1.7% to 31.1%.

Fusion strategies further improve recognition, reaching 44.7%.

LCDNet with MVCP achieves the best recognition performance.

Abstract

Recent advancements in super-resolution for License Plate Recognition (LPR) have sought to address challenges posed by low-resolution (LR) and degraded images in surveillance, traffic monitoring, and forensic applications. However, existing studies have relied on private datasets and simplistic degradation models. To address this gap, we introduce UFPR-SR-Plates, a novel dataset containing 10,000 tracks with 100,000 paired low and high-resolution license plate images captured under real-world conditions. We establish a benchmark using multiple sequential LR and high-resolution (HR) images per vehicle -- five of each -- and two state-of-the-art models for super-resolution of license plates. We also investigate three fusion strategies to evaluate how combining predictions from a leading Optical Character Recognition (OCR) model for multiple super-resolved license plates enhances overall performance. Our findings demonstrate that super-resolution significantly boosts LPR performance, with further improvements observed when applying majority vote-based fusion techniques. Specifically, the Layout-Aware and Character-Driven Network (LCDNet) model combined with the Majority Vote by Character Position (MVCP) strategy led to the highest recognition rates, increasing from 1.7% with low-resolution images to 31.1% with super-resolution, and up to 44.7% when combining OCR outputs from five super-resolved images. These findings underscore the critical role of super-resolution and temporal information in enhancing LPR accuracy under real-world, adverse conditions. The proposed dataset is publicly available to support further research and can be accessed at: https://valfride.github.io/nascimento2024toward/