Although automatic license plate recognition (ALPR) has been studied for decades, the final recognition result can be accurate only if the license plate is detected and the standard format is unambiguous. However, since an image may contain license plates with different formats and scales, license plate detection and standard format classification may fail. In this study, a new ALPR codec framework named EDF-LPR is presented. As for the encoder, at the first stage, candidate license plate characters are detected and recognised directly without considering the format of license plate, and candidate regions of characters are extracted by density-based spatial clustering of applications with noise-like algorithm; at the second stage, poor regions are processed by tilt correction and scale normalisation to obtain more accurate candidate characters. As for the decoder, a sequence learning model is trained to convert each unordered coded sequence into a sequence composed of marks that indicate a way to construct the final result string. Experiments are designed to evaluate the performance of EDF-LPR on both detection rate and recognition rate. The experimental results on public datasets show that the detection rate and recognition rate are 99.51 and 95.3%, respectively, at about 40 fps.

中文翻译：

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EDF-LPR：用于车牌识别的新编码器-解码器框架

尽管对自动车牌识别（ALPR）进行了数十年的研究，但最终的识别结果只有在检测到车牌且标准格式无歧义的情况下才是准确的。但是，由于图像可能包含具有不同格式和比例的牌照，因此牌照检测和标准格式分类可能会失败。在这项研究中，提出了一个新的ALPR编解码器框架，称为EDF-LPR。对于编码器，在第一阶段，不考虑车牌的格式直接检测和识别候选车牌字符，并通过基于噪声的算法对应用程序进行基于密度的空间聚类，提取候选字符区域。在第二阶段，通过倾斜校正和比例尺归一化处理不良区域，以获得更准确的候选字符。对于解码器，训练序列学习模型以将每个无序编码序列转换为由标记组成的序列，这些标记指示构造最终结果字符串的方式。实验旨在评估EDF-LPR在检测率和识别率方面的性能。在公共数据集上的实验结果表明，在约40 fps时，检测率和识别率分别为99.51和95.3％。