Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
This addresses the problem of license plate recognition in challenging, unconstrained surveillance environments for applications like traffic monitoring, though it is incremental as it builds on existing methods.
The paper tackles license plate recognition in unconstrained real-world traffic scenes by using adversarial super-resolution and a one-stage character segmentation method, achieving improved accuracy over current approaches on datasets like AOLP and GIST-LP.
Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes. In this paper, we propose a novel license plate recognition method to handle unconstrained real world traffic scenes. To overcome these difficulties, we use adversarial super-resolution (SR), and one-stage character segmentation and recognition. Combined with a deep convolutional network based on VGG-net, our method provides simple but reasonable training procedure. Moreover, we introduce GIST-LP, a challenging LP dataset where image samples are effectively collected from unconstrained surveillance scenes. Experimental results on AOLP and GIST-LP dataset illustrate that our method, without any scene-specific adaptation, outperforms current LP recognition approaches in accuracy and provides visual enhancement in our SR results that are easier to understand than original data.