License plate recognition based on mathematical morphology and template matching

Lin, Guocong; Xue, Binqiang; Xu, Boyang; Chen, Chuanguang

doi:10.1109/cac48633.2019.8996973

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Precision and recall are used as the evaluation criteria in our experiment [31], as shown in Formulas ( 9) and (10), where TP represents the number of license plates correctly determined by the model, FP represents the number of non-license plates incorrectly determined as license plates by the model, and FN represents the number of license plates incorrectly determined as non-license plates by the model.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Wen

Hua

2022

Wireless Communications and Mobile Computing

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Intelligent transportation applications based on urban Internet of Things can improve the efficiency of government services and promote urban modernization. As smart cameras are more and more widely used in cities, artificial intelligence technology is an important force to achieve license plate recognition. An efficient license plate recognition algorithm not only improves the efficiency of traffic management but also saves management costs. This paper proposes a network based on the shufflenetv2 dilated convolution (SDC) model, which includes two parts: license plate location and license plate recognition. SDC model adopts shufflenetv2 as the backbone network, which combines dilated convolution and global context blocks. Therefore, the receptive field and feature expression ability of the model are enhanced. For license plate location, CIOU loss considers not only the coverage area of the bounding box but also the center distance and aspect ratio. For license plate recognition, CTC loss trains the network based on the sequence and solves the sample alignment problem, which improves the accuracy of license plate recognition. The experiments show that the precision of the SDC model in license plate location is 98.7%, which is 5.2%, 5.5%, and 4.1% higher than the precision of Faster-RCNN, YOLOv3, and SSD, respectively. The precision of the SDC model in license plate recognition is 98.2%, which is 5.3%, 3.7%, and 2.9% higher than the precision of LPRNet, AlexNet, and RPNet, respectively.

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Section: Resultsmentioning

confidence: 99%

“…Before we recognize the license plate, we need to locate the vehicle in the image. Traditional license plate location methods include methods based on edge detection [8], color features [9], and mathematical morphology [10]. However, the above methods are greatly affected by the external environment and image quality.…”

Section: Introductionmentioning

confidence: 99%

Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Wen

Hua

2022

Wireless Communications and Mobile Computing

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“…By using the adaptive output algorithm image processing of the license plate is done. To detect the location of the license plate Lin et al in [21] used a method called a Canny edge operator. To avoid the tilting problem in the license plate perspective transformation algorithm is used.…”

Section: License Plate Detectionmentioning

confidence: 99%

An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks

Andrew¹,

Sebastian²,

Sei³

et al. 2021

AETiC

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One of the largest automotive sectors in the world is India. The number of vehicles traveling by road has increased in recent times. In malls or other crowded places, many vehicles enter and exit the parking area. Due to the increase in vehicles, it is difficult to manually note down the license plate number of all the vehicles passing in and out of the parking area. Hence, it is necessary to develop an Automatic License Plate Detection and Recognition (ALPDR) model that recognize the license plate number of vehicles automatically. To automate this process, we propose a three-step process that will detect the license plate, segment the characters and recognize the characters present in it. Detection is done by converting the input image to a bi-level image. Using region props the characters are segmented from the detected license plate. A two-layer CNN model is developed to recognize the segmented characters. The proposed model automatically updates the details of the car entering and exiting the parking area to the database. The proposed ALPDR model has been tested in several conditions such as blurred images, different distances from the cameras, day and night conditions on the stationary vehicles. Experimental result shows that the proposed system achieves 91.1%, 96.7%, and 98.8% accuracy on license plate detection, segmentation, and recognition respectively which is superior to state-of-the-art literature models.

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“…Extracting text accurately from complex backgrounds that include variations in font, size, color, and orientation poses a significant challenge. Natural images contain multiple objects with diverse sizes and shapes [1]. Text extraction techniques focus on identifying image regions that contain text objects of regular or irregular shapes.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sw Algorithm for Text Extraction and Segmentation in Indian Vehicle License Plate

2023

IRJMETS

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Detecting and recognizing characters on vehicles' license plates has always posed a significant challenge due to various conditions. These conditions include different illumination levels, unavoidable data acquisition noises, and demanding scenarios, such as the need for real-time performance in advanced Intelligent Transportation Systems (ITS) applications. This research paper presents a method for the application of Vehicle License Plate Character Segmentation and Recognition (VLPCSR) that achieves high accuracy and real-time performance. The proposed system is specifically designed for Indian vehicle license plates, which exhibit characteristics such as varying resolutions and layouts, limited digits/characters, diverse background colors, and different font sizes. To address these challenges, the system utilizes the Sliding Window Algorithm (SWA) based on Alexnet, which automatically extracts Persian characters from input images in two steps. A diverse range of vehicle images from various conditions, along with a dataset from Kaggle, have been utilized for training and testing. The practical application of the system has been installed in real-life scenarios. Experimental results demonstrate an impressive end-to-end accuracy of 96.33% on a dataset of 300 images. The test data includes both color and grayscale images capturing vehicles at different distances and shooting angles, with variations in brightness and resolution. Furthermore, the system exhibits excellent performance in terms of recall, precision, and the Harmonic mean, making it well-suited for real-world data.

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License plate recognition based on mathematical morphology and template matching

Cited by 10 publications

References 15 publications

Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

Vehicle License Plate Recognition Using Shufflenetv2 Dilated Convolution for Intelligent Transportation Applications in Urban Internet of Things

An Intelligent License Plate Detection and Recognition Model Using Deep Neural Networks

Enhanced Sw Algorithm for Text Extraction and Segmentation in Indian Vehicle License Plate

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