Abstract
License Plate Character Recognition (LPCR) is a technology for reading vehicle registration plates using optical character recognition from images and videos, and it has a long history due to its usefulness. While LPCR has been significantly improved with the advance of deep learning, training deep networks for LPCR module requires a large number of license plate (LP) images and their annotations. Unlike other public datasets of vehicle information, each LP has a unique combination of characters and numbers depending on the country or the region. Therefore, collecting a sufficient number of LP images is extremely difficult for normal research. In this paper, we propose LP-GAN, an LP image generation method, by applying an ensemble of generative adversarial networks (GAN), and we also propose a modified lightweight YOLOv2 model for an efficient end-to-end LPCR module. With only 159 real LP images available online, thousands of synthetic LP images were generated by using LP-GAN. The generated images not only looked similar to real ones, but they were also shown to be effective for training the LPCR module. As a result of performance tests with 22,117 real LP images, the LPCR module trained with only the generated synthetic dataset achieved 98.72% overall accuracy, which is comparable to that of training with a real LP image dataset. In addition, we improved the processing speed of LPCR about 1.7 times faster than that of the original YOLOv2 model by using the proposed lightweight model.
Highlights
License plate (LP) character information is uniquely assigned so that each vehicle on the road can be identified
To confirm whether the LP images from the three LP-generative adversarial networks (GAN) generators together enhanced the performance of the License plate character recognition (LPCR) more than each on its own, two ensemble datasets were prepared from all three sources: the Ensemble_9k dataset was combined with pix2pix_cGAN_3k, CycleGAN_3k, and StarGAN_3k and the Ensemble_3k dataset was combined with 1000 images randomly selected from each of the pix2pix_cGAN_3k, CycleGAN_3k, and StarGAN_3k datasets
The modified YOLOv2 detector proposed in this paper simultaneously performs the location and classification of the object of interest, but the LPCR module obtains the same result if the character classification is correct, even if the location of the character is incorrect
Summary
License plate (LP) character information is uniquely assigned so that each vehicle on the road can be identified. The end-to-end LPCR method using an object detector based on convolutional neural network (CNN) can recognize LP character information by performing character segmentation and character recognition simultaneously from the LP image. An LP generator based on GAN was made by a small set of real LP images, and generating realistic LP images that have the desired character information for use as training data for the LPCR module of the ALPR system.
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