Enhance of License Plate Matching Procedure Quality Using Augmented Probability Matrix Method
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F25%3A00384121" target="_blank" >RIV/68407700:21260/25:00384121 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/SCSP65598.2025.11037719" target="_blank" >https://doi.org/10.1109/SCSP65598.2025.11037719</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SCSP65598.2025.11037719" target="_blank" >10.1109/SCSP65598.2025.11037719</a>
Alternative languages
Result language
angličtina
Original language name
Enhance of License Plate Matching Procedure Quality Using Augmented Probability Matrix Method
Original language description
The issue of traffic data quality is an important part of the Smart City concept. Accurate and high quality traffic data serves cities not only for detailed traffic insights, online and offline traffic management, but also for future decision making on the im-plementation of strategic traffic measures. This paper focuses specifically on the processing and evaluation of data from constantly developing license plate recognition (LPR) systems and aims to develop a method that allows comparing text strings of license plate characters from different vehicle records and, without knowing the real (correct) shape of the license plate, determine whether the compared records belong to the same vehi-cle or not - in other words, whether they should be matched. The paper builds on previous research and a quality improve-ment method based on a probabilistic model for two measurement profiles. This paper focuses on the application, actualization and extension of the mentioned method to LPR systems with arbitrary number of m
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10700 - Other natural sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 Smart City Symposium Prague - IEEE proceedings
ISBN
979-8-3315-2551-4
ISSN
2831-5618
e-ISSN
2691-3666
Number of pages
7
Pages from-to
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Publisher name
IEEE Press
Place of publication
New York
Event location
Prague
Event date
May 29, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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