The recognition of license plates is usually considered a rather simple task, that a human
is perfectly capable of. However, there exist many factors (e.g. fog, rain), that can
signi cantly worsen the image quality and therefore increase the di culty of recognizing
a license plate. In addition, further factors e.g. low resolution or small size of the license
plate section may increase the di culty up to a point, where even humans are unable to
identify it.
A possible approach to solve this problem is to build and train a neural network using
collected image data. In theory, this should yield a high success rate and outperform a
human. However, a huge number of images, that also ful ll certain criteria, is needed in
order to reliably recognize plates in dierent situations.
That is the reason why this thesis aims at building and training a neural network, that is
based on an existing CNN [1], for recognizing license plates using training data, which is
arti cially created. This ensures enough images are provided, while facilitating the possibility
of adding image eects to simulate many possible situations. The needed images
can be created using Blender: It oers the option to create a 3D model of a license plate,
as well as options to simulate certain weather conditions like fog or rain, while also providing
an API to automate the creation process. This way, nearly all cases can be covered
and the described procedure maximizes the success rate of the license plate detection.
The thesis consists of the following steps:
Creating a training data set consisting of generated license plate images (Blender
Python API)
Fitting the parameters of the Deep Learning model
Evaluation of the model t on datasets with real license plate images
Literatur
[1] Benedikt Lorch, Shruti Agarwal, and Hany Farid. Forensic Reconstruction of Severely
Degraded License Plates. In Society for Imaging Science & Technology, editor,
Electronic Imaging, Jan 2019.