Automatic License-plate Recognition using Image Segmentation & Processing - Anisha P R, Kishor Kumar Reddy C, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Low-quality surveillance cameras throughout the cities could provide important cues to identify a suspect, for example, in a crime scene. However, the license-plate recognition is especially difficult under poor image resolutions. In this vein, super-resolution (SR) can be an inexpensive solution, via software, to overcome this limitation. Consecutive frames in a video may contain different information that could be integrated into a single image, richer in details. In this paper, we design and develop a novel, free and open-source framework underpinned by SR and automatic license-plate recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed specifically for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, super-resolve, and recognize it‟s alphanumeric. The user receives as outputs the rectified and super-resolved license-plate, richer in detail, and also the sequence of licenseplates characters that have been automatically recognized in the super-resolved image. Our experiments show that SR can indeed increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions..