HIFA-LPR: High-Frequency Augmented License Plate Recognition in Low-Quality Legacy Conditions via Gradual End-to-End Learning

Sung-Jin Lee, Jun-Seok Yun, Eung Joo Lee and Seok Bong Yoo
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Sung-Jin Lee: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea
Jun-Seok Yun: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea
Eung Joo Lee: Department of Radiology, MGH and Harvard Medical School, Boston, MA 02115, USA
Seok Bong Yoo: Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea

Mathematics, 2022, vol. 10, issue 9, 1-24

Abstract: Scene text detection and recognition, such as automatic license plate recognition, is a technology utilized in various applications. Although numerous studies have been conducted to improve recognition accuracy, accuracy decreases when low-quality legacy license plate images are input into a recognition module due to low image quality and a lack of resolution. To obtain better recognition accuracy, this study proposes a high-frequency augmented license plate recognition model in which the super-resolution module and the license plate recognition module are integrated and trained collaboratively via a proposed gradual end-to-end learning-based optimization. To optimally train our model, we propose a holistic feature extraction method that effectively prevents generating grid patterns from the super-resolved image during the training process. Moreover, to exploit high-frequency information that affects the performance of license plate recognition, we propose a license plate recognition module based on high-frequency augmentation. Furthermore, we propose a gradual end-to-end learning process based on weight freezing with three steps. Our three-step methodological approach can properly optimize each module to provide robust recognition performance. The experimental results show that our model is superior to existing approaches in low-quality legacy conditions on UFPR and Greek vehicle datasets.

Keywords: gradual end-to-end learning; single-image super-resolution; automatic license plate recognition; low-quality legacy conditions; holistic feature extraction; high-frequency augmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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