Authors:
Markus Russold
;
Martin Nocker
and
Pascal Schöttle
Affiliation:
MCI The Entrepreneurial School, Innsbruck, Austria
Keyword(s):
Automatic License Plate Recognition, Continual Learning, Synthetic Data Generation, Computer Vision.
Abstract:
In the realm of image processing, deep neural networks (DNNs) have proven highly effective, particularly in tasks such as license plate recognition. However, a notable limitation in their application is the dependency on the quality and availability of training data, a frequent challenge in practical settings. Addressing this, our research involves the creation of a comprehensive database comprising over 45,000 license plate images, meticulously designed to reflect real-world conditions. Diverging from conventional character-based approaches, our study centers on the analysis of entire license plates using machine learning algorithms. This novel approach incorporates continual learning and dynamic network adaptation techniques, enhancing existing automatic license plate recognition (ALPR) systems by boosting their overall confidence levels. Our findings validate the utility of machine learning in ALPR, even under stringent constraints, and demonstrate the feasibility and efficiency o
f recognizing license plates as complete units.
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