Method of segmentation and recognition of Ukrainian license plates

Radchenko, Andrii; Zarovsky, Ruslan; Kazymyr, Volodymyr

doi:10.1109/ysf.2017.8126593

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…Cyrillic has a separate letter set but is still relatively comparable to Latin. The Cyrillic writing system has been adopted by certain Asian and Eastern European languages, including Russian, Bulgarian, Ukrainian, and Macedonian, where the recognition rate is recorded for them as follows: Russian 83.42% [26], Bulgarian 89.4% [27], Ukrainian 95% [28], and Macedonian 93% [29].…”

Section: Alphabetic Systemmentioning

confidence: 99%

Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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Character recognition (CR) systems were attracted by a massive number of authors’ interest in this field, and lot of research has been proposed, developed, and published in this regard with different algorithms and techniques due to the great interest and demand of raising the accuracy of the recognition rate and the reliability of the presented system. This work is proposed to provide a guideline for CR system construction to afford a clear view to the authors on building their systems. All the required phases and steps have been listed and clarified within sections and subsections along with detailed graphs and tables beside the possibilities of techniques and algorithms that might be used, developed, or merged to create a high-performance recognition system. This guideline also could be useful for readers interested in this field by helping them extract the information from such papers easily and efficiently to reach the main structure along with the differences between the systems. In addition, this work recommends to researchers in this field to comprehend a specified categorical table in their work to provide readers with the main structure of their work that shows the proposed system’s structural layout and enables them to easily find the information and interests.

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Section: Alphabetic Systemmentioning

confidence: 99%

Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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“…A number of commercial software is developed in this area. Still, they cannot be readily used when vehicle image is delivered in different styles and formats [1][2][3]. Proposed approach allows removing this disadvantage by ensemble of two methods: (I) detection and extraction of image region included license plate from source images flow and (ii) recognition of character presented on the license plate.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Should the information suggest that there is anything suspicious about the vehicle, applicable actions can be taken. The algorithm of license plate recognition (LPR) consists of the following steps: (i) to capture the car's images, (ii) to de-blur of image frames, (iii) to extract image of license plate, (iv) to extract characters from license plate image, (v) to recognize license plate characters and identify the vehicle [1]…”

Section: Literature Reviewmentioning

confidence: 99%

License plate detection using AI

Obulakshmi¹,

Ramya²,

Mythili³

2022

ijhs

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In today's world, withincreasing population number of vehiclesare also increased. Monitoring each andevery vehicle in roadside by manualforce becomes a tedious job. For effective traffic management some solution is required. The traditional system of licenseplate recognition basically relies on themorphological processing of images. The accuracy of recognition is also lower. Inorder to provide an efficient solution forlicense plate position, we propose a new method of license plate recognition. In the license plate position, we use traditional positioning method and Haar Cascade algorithm to detect the licenseplate. In addition with the traditionalidentification system cutting and matching, we use the ability feature extraction of Convolution Neural Network (CNN) to detect the license platedirectly, which avoids the recognitionerror which is caused by the segmentation in the license platerecognition. In this project, we propose a new license plate recognition system. It is divided into two parts, they are licenseplate positioning and characterrecognition.

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“…For images with challenging backgrounds, the features given may not work. In [15] developed a technique for character segmentation using a binarized input image without character shapes or presence among the characters improves the performance quite a little. It is thus exceedingly difficult to use a binarization model that distinguishes foreground and background data in images consisting of complicated backgrounds.…”

Section: A Character Segmentationmentioning

confidence: 99%

Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

Naidu¹,

Thiruvengatanadhan²,

Narayana³

et al. 2022

IJACSA

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The number plate recognition system must be able to quickly and accurately identify the plate in both low and noisy lighting conditions, as well as within the specified time limit. This study proposes automated authentication, which would minimize security and individual workload while eliminating the requirement for human credential verification. The four processes that follow the acquisition of an image are preprocessing, number plate localization, character segmentation, and character identification. A human error during the affirmation and the enrolling process is a distinct possibility since this is a manual approach. Personnel at the selected location may find it difficult and time-consuming to register and compose information manually. Due to the printed edition design, it is impossible to communicate the information. Character segmentation breaks down the number plate region into individual characters, and character recognition detects the optical characters. Our approach was tested using genuine license plate images under various environmental circumstances and achieved overall recognition accuracy of 91.54% with a single license plate in an average duration of 2.63 seconds.

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Method of segmentation and recognition of Ukrainian license plates

Cited by 4 publications

References 2 publications

Construction of Alphabetic Character Recognition Systems: A Review

Construction of Alphabetic Character Recognition Systems: A Review

License plate detection using AI

Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

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