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.

授权号码识别（LPR）在收费、停车管理和交通执法等各种应用中起着重要作用。虽然通过深度学习的发展，LPR已取得了重大进展，但目前缺乏研究探索融合多个识别模型的潜在改进结果。本研究旨在通过研究使用直接方法组合多达12个不同模型，如选择最可靠的预测或采用多数投票策略，来填补这一空白。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好处。本质上，多模型融合大大降低了在特定数据集/场景上获得次优性能的可能性。我们还发现，根据模型的速度进行组合是一种吸引人的方法。具体而言，对于识别任务可以容忍一些额外时间，但不能过多的应用场景，有效的策略是组合4-6个模型。这些模型可能不是单独最准确的，但它们的融合在准确性和速度之间达到了最佳平衡。

利用模型融合提升车牌识别