Skewness and obliqueness of vehicle plate images influence license plate recognition. The more tilted plate images are, the harder the recognition task is. To this end, if plate images are preprocessed to be aligned and rectified, the recognition performance would improve. We propose deep neural network models that can locate four corner plate positions, which can then be used to perform the perspective transformation that can be used to rectify plates. Such a transformation is called homography. The models consist of two sequential parts: a feature extraction part having convolution and a regression part with fully connected layers. The models are open in the sense that the feature extraction part can host other well-known models such as Mobilenet as long as they have the feature capture capability. We devise a loss function as the sum of Euclidean distance between predicted coordinates and ground truth and discuss image augmentation schemes. The experiment results show that the models with well-known object detection models are able to predict corner positions with relatively high precision.

中文翻译：

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用于校正倾斜车牌图像的深角预测

车牌图像的偏斜和倾斜影响车牌识别。倾斜的车牌图像越多，识别任务就越困难。为此，如果对车牌图像进行预处理以进行对齐和校正，识别性能将会提高。我们提出了可以定位四个角板位置的深度神经网络模型，然后可用于执行可用于校正板的透视变换。这种变换称为单应性。这些模型由两个连续的部分组成：具有卷积的特征提取部分和具有完全连接层的回归部分。模型是开放的，因为特征提取部分可以托管其他知名模型，例如 Mobilenet，只要它们具有特征捕获能力。我们将损失函数设计为预测坐标和地面实况之间的欧几里得距离之和，并讨论图像增强方案。实验结果表明，具有众所周知的物体检测模型的模型能够以较高的精度预测角点位置。