  License Plate Recognition (LPR) plays a critical role in various
applications, such as toll collection, parking management, and traffic law
enforcement. Although LPR has witnessed significant advancements through the
development of deep learning, there has been a noticeable lack of studies
exploring the potential improvements in results by fusing the outputs from
multiple recognition models. This research aims to fill this gap by
investigating the combination of up to 12 different models using
straightforward approaches, such as selecting the most confident prediction or
employing majority vote-based strategies. Our experiments encompass a wide
range of datasets, revealing substantial benefits of fusion approaches in both
intra- and cross-dataset setups. Essentially, fusing multiple models reduces
considerably the likelihood of obtaining subpar performance on a particular
dataset/scenario. We also found that combining models based on their speed is
an appealing approach. Specifically, for applications where the recognition
task can tolerate some additional time, though not excessively, an effective
strategy is to combine 4-6 models. These models may not be the most accurate
individually, but their fusion strikes an optimal balance between speed and
accuracy.
