Abstract. In vehicle monitoring, recognizing graphic vehicle identification number (VIN) on the car frame is a particularly important step. While text recognition methods have made great progress, automatic graphic vehicle VIN recognition is still challenging. In VIN images, the VIN text is engraved on the car frame, with complex background and arbitrary orientation, which make it extremely difficult for recognition. We propose an efficient framework for recognizing rotational VIN. First, combining lightweight convolutional neural network and per-pixel segmentation in the output layer, we achieve fast and excellent VIN detection. Second, we take the VIN recognition task as a sequential position-dependent classification problem. By attaching sequential classifiers, we predict VIN text without character segmentation. Finally, we introduce a VIN dataset, which contains 2000 raw rotational VIN images and 90,000 horizontal VIN images for validating our framework. Experiments results show that the framework we proposed achieves good performance in VIN detection and recognition. By automatically identifying the VIN, we can quickly confirm the vehicle’s identity and help vehicle monitoring and tracking.

中文翻译：

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用于图形车辆识别号识别的具有顺序分类的高效框架

摘要。在车辆监控中，识别车架上的图形车辆识别码（VIN）是一个特别重要的步骤。虽然文本识别方法取得了很大进步，但自动图形车辆 VIN 识别仍然具有挑战性。在VIN图像中，VIN文字刻在车架上，背景复杂，方向随意，识别难度极大。我们提出了一个有效的框架来识别旋转 VIN。首先，在输出层结合轻量级卷积神经网络和逐像素分割，我们实现了快速且出色的 VIN 检测。其次，我们将 VIN 识别任务视为一个顺序位置相关的分类问题。通过附加顺序分类器，我们无需字符分割即可预测 VIN 文本。最后，我们引入了一个 VIN 数据集，其中包含 2000 个原始旋转 VIN 图像和 90,000 个水平 VIN 图像，用于验证我们的框架。实验结果表明，我们提出的框架在 VIN 检测和识别方面取得了良好的性能。通过自动识别VIN，我们可以快速确认车辆身份，帮助车辆监控和跟踪。