Vehicle license plate recognition is a crucial task in intelligent traffic
management systems. However, the challenge of achieving accurate recognition
persists due to motion blur from fast-moving vehicles. Despite the widespread
use of image synthesis approaches in existing deblurring and recognition
algorithms, their effectiveness in real-world scenarios remains unproven. To
address this, we introduce the first large-scale license plate deblurring
dataset named License Plate Blur (LPBlur), captured by a dual-camera system and
processed through a post-processing pipeline to avoid misalignment issues.
Then, we propose a License Plate Deblurring Generative Adversarial Network
(LPDGAN) to tackle the license plate deblurring: 1) a Feature Fusion Module to
integrate multi-scale latent codes; 2) a Text Reconstruction Module to restore
structure through textual modality; 3) a Partition Discriminator Module to
enhance the model's perception of details in each letter. Extensive experiments
validate the reliability of the LPBlur dataset for both model training and
testing, showcasing that our proposed model outperforms other state-of-the-art
motion deblurring methods in realistic license plate deblurring scenarios. The
dataset and code are available at this https URL

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