Department of Software Engineering, University of Sialkot, Sialkot 51040, Pakistan.
Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22044, Pakistan.
Sensors (Basel). 2021 Nov 19;21(22):7696. doi: 10.3390/s21227696.
License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.
车牌定位是指找到车牌区域并绘制其边界框的过程，而识别则是指识别边界框内的文本的过程。当前最先进的车牌定位和识别方法需要标准尺寸、样式、字体和颜色的车牌。不幸的是，在巴基斯坦，车牌是非标准的，在上述特征方面存在差异。本文提出了一种基于深度学习的方法，用于定位和识别具有非均匀和非标准化尺寸、字体和样式的巴基斯坦车牌。我们开发了一个新的巴基斯坦车牌数据集 (PLPD) 来训练和评估所提出的模型。我们进行了广泛的实验来比较所提出的方法与现有技术的准确性。结果表明，所提出的方法在定位和识别非标准车牌方面优于其他方法。
