Incremental Whole Plate ALPR Under Data Availability Constraints

Markus Russold, Martin Nocker, Pascal Schöttle

2024

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 of recognizing license plates as complete units.

Paper Citation

in Harvard Style

Russold M., Nocker M. and Schöttle P. (2024). Incremental Whole Plate ALPR Under Data Availability Constraints. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 131-140. DOI: 10.5220/0012566400003654

in Bibtex Style

@conference{icpram24,
author={Markus Russold and Martin Nocker and Pascal Schöttle},
title={Incremental Whole Plate ALPR Under Data Availability Constraints},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={131-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012566400003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Incremental Whole Plate ALPR Under Data Availability Constraints
SN - 978-989-758-684-2
AU - Russold M.
AU - Nocker M.
AU - Schöttle P.
PY - 2024
SP - 131
EP - 140
DO - 10.5220/0012566400003654
PB - SciTePress