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。

基于多角度视角模型的车牌识别

License Plate Recognition Based On Multi-Angle View Model

In surveillance, accurately recognizing license plates is hindered by their
often low quality and small dimensions, compromising recognition precision.
Despite advancements in AI-based image super-resolution, methods like
Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs)
still fall short in enhancing license plate images. This study leverages the
cutting-edge diffusion model, which has consistently outperformed other deep
learning techniques in image restoration. By training this model using a
curated dataset of Saudi license plates, both in low and high resolutions, we
discovered the diffusion model's superior efficacy. The method achieves a
12.55\% and 37.32% improvement in Peak Signal-to-Noise Ratio (PSNR) over SwinIR
and ESRGAN, respectively. Moreover, our method surpasses these techniques in
terms of Structural Similarity Index (SSIM), registering a 4.89% and 17.66%
improvement over SwinIR and ESRGAN, respectively. Furthermore, 92% of human
evaluators preferred our images over those from other algorithms. In essence,
this research presents a pioneering solution for license plate
super-resolution, with tangible potential for surveillance systems.

该研究利用最先进的扩散模型在低分辨率和高分辨率的沙特车牌图像上进行训练，实现了超分辨率处理，其在识别精度和结构相似性指数方面都超过了现有技术，并获得了 92％的人工评估员的认可，为监控系统提供了具有实际潜力的车牌超分辨率的创新解决方案。

基于扩散模型的车牌超分辨率

License Plate Super-Resolution Using Diffusion Models

Millions of vehicles are transported every year, tightly parked in vessels or
boats. To reduce the risks of associated safety issues like fires, knowing the
location of vehicles is essential, since different vehicles may need different
mitigation measures, e.g. electric cars. This work is aimed at creating a
solution based on a nano-drone that navigates across rows of parked vehicles
and detects their license plates. We do so via a wall-following algorithm, and
a CNN trained to detect license plates. All computations are done in real-time
on the drone, which just sends position and detected images that allow the
creation of a 2D map with the position of the plates. Our solution is capable
of reading all plates across eight test cases (with several rows of plates,
different drone speeds, or low light) by aggregation of measurements across
several drone journeys.

使用纳米无人机通过沿墙寻随算法和卷积神经网络实时检测停放车辆的车牌，并生成包含车牌位置的 2D 地图。