  With the robust development of technology, license plate recognition
technology can now be properly applied in various scenarios, such as road
monitoring, tracking of stolen vehicles, detection at parking lot entrances and
exits, and so on. However, the precondition for these applications to function
normally is that the license plate must be 'clear' enough to be recognized by
the system with the correct license plate number. If the license plate becomes
blurred due to some external factors, then the accuracy of recognition will be
greatly reduced. Although there are many road surveillance cameras in Taiwan,
the quality of most cameras is not good, often leading to the inability to
recognize license plate numbers due to low photo resolution. Therefore, this
study focuses on using super-resolution technology to process blurred license
plates. This study will mainly fine-tune three super-resolution models:
Real-ESRGAN, A-ESRGAN, and StarSRGAN, and compare their effectiveness in
enhancing the resolution of license plate photos and enabling accurate license
plate recognition. By comparing different super-resolution models, it is hoped
to find the most suitable model for this task, providing valuable references
for future researchers.
