License Plate Recognition System Based on OpenCV

Zhang, Chao; Tai, Yonghang; Li, Qiong; Jiang, Tao; Mao, Weimin; Dong, Hao

doi:10.1007/978-981-16-3391-1_28

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…In image processing, gaussian smoothing is frequently utilized for noise reduction and the avoidance of the goal of Gaussian smoothing is to create a flat image by using the nonlinear low-pass noise in the image signal. The goal of Gaussian smoothing is to produce a flat [27][28][29][30][31], gradual image with less noise by using a Gaussian filter to remove noise from the image signal [9]. A 55 Gaussian filter was used in this design to apply the Gaussian smoothing.…”

Section: Scale Changementioning

confidence: 99%

Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Yan

Wanting

Liu

et al. 2022

MIST

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This study proposed an intelligent algorithm based on digital image processing and character recognition to address the current situation in which the National Medical Products Administration promulgated the relevant regulations on the complete prohibition of the use of mercury sphygmomanometers in 2020 and the currently widely used electronic sphygmomanometers need to undergo regular verification and quality testing. The intelligent algorithm made it possible to automatically acquire the electronic sphygmomanometer indication value during quality assurance or verification. The images of the electronic sphygmomanometer were captured using a Raspberry Pi-connected video camera head; in the software development, automatic detection of the electronic sphygmomanometer's indication value was achieved by running the computer vision-based OpenCV library on the Raspberry Pi and utilizing image preprocessing techniques like scale transformation, grayscale conversion, Gaussian smoothing and edge detection, and character segmentation. The development of intelligent devices for the automatic verification of electronic sphygmomanometers has a technical foundation in the research and design of the digital recognition algorithm, and it has a certain reference value for character recognition of electronic instruments or the creation of automatic instrument indication value recording devices.

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Section: Scale Changementioning

confidence: 99%

Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Yan

Wanting

Liu

et al. 2022

MIST

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“…For anonymization, we applied algorithms from the OpenCV library [24,25], but these algorithms focus on the process of "reading" the license plate once the image has been segmented and the region of interest (ROI) has been located. In our case, this is the most complex part since the portion of the license plate in the image is very small, so the segmentation can generate too much noise likely to be considered license plate.…”

Section: Techniques For Anonymizing the Datasetmentioning

confidence: 99%

Dataset: Traffic Images Captured from UAVs for Use in Training Machine Vision Algorithms for Traffic Management

Rosende

Ghisler²,

Andrés

et al. 2022

Data

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A dataset of Spanish road traffic images taken from unmanned aerial vehicles (UAV) is presented with the purpose of being used to train artificial vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which involves the acquisition of the data and images, the labeling of the vehicles, anonymization, data validation by training a simple neural network model, and the description of the structure and contents of the dataset (which amounts to 15,070 images). The images were captured by drones (but would be similar to those that could be obtained by fixed cameras) in the field of intelligent vehicle management. The presented dataset is available and accessible to improve the performance of road traffic vision and management systems since there is a lack of resources in this specific domain.

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