用深度学习提升车牌检测与识别准确率，还分析了字体影响
License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation
- 用Faster R-CNN检测车牌，CNN-RNN+CTC识别字符
- 在多个数据集上达到90%以上召回率，关键依赖字体特征
- 揭示字体差异对识别的影响，适合做智能交通系统优化
车牌检测（LPD）对交通管理、车辆追踪和执法至关重要，但受光照变化和字体多样性的挑战。传统方法依赖图像处理和机器学习，现逐步转向深度学习以提升鲁棒性。现有方法常需针对特定地区数据集调整。本文提出双阶段深度学习策略：采用Faster R-CNN进行检测，使用基于MobileNet V3的CNN-RNN模型结合连接时序分类（CTC）损失进行字符识别。该方法在安大略、魁北克、加州和纽约州数据集上训练，于中心模式识别与机器智能研究所（CENPARMI）数据集实现92%召回率，在UFPR-ALPR数据集上达90%。研究还对司机哥特体、恐怖号体、加州克拉伦登体、苏黎世超窄体等字体进行评估，发现字体特征显著影响识别性能，通过错误分析揭示误检原因，为未来车牌识别系统优化提供依据。
原文摘要 · Abstract (English)
License plate detection (LPD) is essential for traffic management, vehicle tracking, and law enforcement but faces challenges like variable lighting and diverse font types, impacting accuracy. Traditionally reliant on image processing and machine learning, the field is now shifting towards deep learning for its robust performance in various conditions. Current methods, however, often require tailoring to specific regional datasets. This paper proposes a dual deep learning strategy using a Faster R-CNN for detection and a CNN-RNN model with Connectionist Temporal Classification (CTC) loss and a MobileNet V3 backbone for recognition. This approach aims to improve model performance using datasets from Ontario, Quebec, California, and New York State, achieving a recall rate of 92% on the Centre for Pattern Recognition and Machine Intelligence (CENPARMI) dataset and 90% on the UFPR-ALPR dataset. It includes a detailed error analysis to identify the causes of false positives. Additionally, the research examines the role of font features in license plate (LP) recognition, analyzing fonts like Driver Gothic, Dreadnought, California Clarendon, and Zurich Extra Condensed with the OpenALPR system. It discovers significant performance discrepancies influenced by font characteristics, offering insights for future LPD system enhancements. Keywords: Deep Learning, License Plate, Font Evaluation
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