Automatic License-plate Recognition using Image Segmentation & Processing

Abstract

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..