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 accuracy and speed.

翻译：车牌识别（LPR）在各种应用中发挥着关键作用，例如收费管理、停车管理和交通执法。尽管随着深度学习的发展，LPR取得了显著进展，但探索通过融合多个识别模型输出来改善结果潜力的研究仍然明显缺乏。本研究旨在填补这一空白，通过使用直接方法（如选择最置信的预测或采用基于多数投票的策略）对多达12个不同模型进行组合研究。我们的实验涵盖了广泛的数据集，揭示了融合方法在数据集内部和跨数据集设置中的显著优势。本质上，融合多个模型大大降低了在特定数据集/场景中表现不佳的可能性。我们还发现，基于速度组合模型是一种有吸引力的方法。具体而言，对于识别任务可容忍一定额外处理时间（但不宜过长）的应用，一个有效的策略是组合4-6个模型。这些模型单独来看可能并非最准确，但它们的融合在准确性和速度之间实现了最佳平衡。