Identifying a car by its License Plate (LP) is a critical task in many applications, such as travel time estimation, vehicle re-identification, automatic toll collection, etc. Therefore, matching them must be as accurate as possible. This research proposes a novel deep neural network based method and its learning strategy for LP matching (LPM), which is originally from the cognitive-psychology-inspired unified objective function. The proposed method uses a deep Convolutional Neural Network (CNN) model to extract effective visual signature of the LP image. It exploits the multi-task learning approach to optimize the model, which combines two different tasks: (a) parallel letters recognition to transcribe the image-text contents and (b) image classification to classify the distinct LPs. Moreover, it takes profit from the use of image augmentation techniques. The proposed method is evaluated on three datasets of different characteristics. One of them was collected in this research and will be released publicly. The obtained results show that the proposed method performs better than the state-of-the art methods based on the commonly used evaluation metrics and computation time.

在许多应用中，例如行驶时间估计，车辆重新识别，自动收费等，通过车牌（LP）识别汽车是一项关键任务。因此，匹配它们必须尽可能准确。这项研究提出了一种新颖的基于深度神经网络的LP匹配（LPM）方法及其学习策略，该方法最初来自于认知心理学启发的统一目标函数。所提出的方法使用深度卷积神经网络（CNN）模型来提取LP图像的有效视觉签名。它利用多任务学习方法来优化模型，该模型结合了两个不同的任务：（a）并行字母识别以记录图像文本内容；（b）图像分类以对不同的LP进行分类。而且，它通过使用图像增强技术获利。该方法在三个具有不同特征的数据集上进行了评估。其中一项是在这项研究中收集的，将公开发布。获得的结果表明，基于常用的评估指标和计算时间，该方法的性能优于最新方法。