In the realm of research, the detection/recognition of text within
images/videos captured by cameras constitutes a highly challenging problem for
researchers. Despite certain advancements achieving high accuracy, current
methods still require substantial improvements to be applicable in practical
scenarios. Diverging from text detection in images/videos, this paper addresses
the issue of text detection within license plates by amalgamating multiple
frames of distinct perspectives. For each viewpoint, the proposed method
extracts descriptive features characterizing the text components of the license
plate, specifically corner points and area. Concretely, we present three
viewpoints: view-1, view-2, and view-3, to identify the nearest neighboring
components facilitating the restoration of text components from the same
license plate line based on estimations of similarity levels and distance
metrics. Subsequently, we employ the CnOCR method for text recognition within
license plates. Experimental results on the self-collected dataset
(PTITPlates), comprising pairs of images in various scenarios, and the publicly
available Stanford Cars Dataset, demonstrate the superiority of the proposed
method over existing approaches.

该篇论文提出了一种针对车牌内文本检测问题的方法，通过合并多个不同角度的图像帧，从车牌的角点和面积等特征中提取描述性特征，使用相似度估计和距离度量来恢复同一车牌行中的文本组件。随后，采用 CnOCR 方法进行车牌内文本识别。对自收集的数据集 (PTITPlates) 和公开的 Stanford Cars 数据集进行的实验结果表明，该方法在各种情景下都优于现有方法。