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Title:An overview of image analysis algorithms for license plate recognition
Authors:ID Aboura, Khalid (Author)
ID Al-Hmouz, Rami (Author)
Files:.pdf Organizacija_2017_Aboura,_Al-Hmouz_An_Overview_of_Image_Analysis_Algorithms_for_License_Plate_Recognition.pdf (1,01 MB)
MD5: A77C1CA468BEB6602E69AEFF170C2FB7
PID: 20.500.12556/dkum/1940c34d-6001-4efc-8df3-57a99fb8c7be
 
URL http://www.degruyter.com/view/j/orga.2017.50.issue-3/orga-2017-0014/orga-2017-0014.xml
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FOV - Faculty of Organizational Sciences in Kranj
Abstract:Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.
Keywords:image analysis, probabilistic modeling, signal processing, license plate recognition
Publication status:Published
Publication version:Version of Record
Year of publishing:2017
Number of pages:str. 285-295
Numbering:Letn. 50, št. 3
PID:20.500.12556/DKUM-69071 New window
ISSN:1318-5454
UDC:656.1:004
ISSN on article:1318-5454
COBISS.SI-ID:4429768 New window
DOI:10.1515/orga-2017-0014 New window
NUK URN:URN:SI:UM:DK:BK4I2XGV
Publication date in DKUM:28.11.2017
Views:1501
Downloads:367
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
ABOURA, Khalid and AL-HMOUZ, Rami, 2017, An overview of image analysis algorithms for license plate recognition. Organizacija : revija za management, informatiko in kadre [online]. 2017. Vol. 50, no. 3, p. 285–295. [Accessed 24 April 2025]. DOI 10.1515/orga-2017-0014. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=69071
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Record is a part of a journal

Title:Organizacija : revija za management, informatiko in kadre
Shortened title:Organizacija
Publisher:Moderna organizacija
ISSN:1318-5454
COBISS.SI-ID:610909 New window

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:28.11.2017

Secondary language

Language:Slovenian
Title:Pregled algoritmov za analizo slike za prepoznavanje registrske tablice
Abstract:Ozadje in namen: V članku raziskujemo problem prepoznavanja registrskih tablic (LPR), in podamo pregled števil­nih algoritmov, ki jih lahko uporabimo pri problemih analize slik. V sistemih za podporo vodenju, ki uporabljajo za prepoznavanje slikovnih objektov, je inteligenca vgrajena v statistične algoritme, ki jih je mogoče uporabiti v različnih korakih razpoznavanja. Opisujemo več rešitev, od začetnega koraka do lokalizacije in prepoznavanja slikovnih el­ementov. Cilj tega prispevka je predstaviti več verjetnostnih pristopov v korakih razpoznavanja, nato pa združiti te pristope v en sistem. Večina pristopov uporablja deterministične modele, ki so občutljivi na številne nenadzorovane vplive, kot so osvetlitev, razdalja vozila do kamere, šum pri procesiranju itd. Bistvo naših pristopov je v statističnih algoritmih, ki lahko natančno lokalizirajo in prepoznajo registrsko tablico. Oblikovanje / metodologija / pristop: Predstavimo enostavne in poceni metode za reševanje relativno pomemb­nih problemov z uporabo verjetnostnih pristopov. Pri teh pristopih opisujemo številne statistične rešitve od stopnje začetnega praga do lokalizacije in prepoznavanja slikovnih elementov. V koraku lokalizacije uporabljamo frekvenčne signale iz slik registrskih tablic, ki jih analiziramo z uporabo diskretne Fourier-jeve transformacije. Pri prepoznavanju znakov na tablicah smo uporabili tudi verjetnostni model. Na koncu prikazujemo, kako združiti rezultate iz dvojezičnih tablic, kot so na primer tablice Saudove Arabije. Rezultati: Algoritmi so učinkoviti pri razpoznavanju znakov na vozilih, v stavbah in drugod. Rezultat kaže prednost uporabe verjetnostnega pristopa v vseh korakih razpoznavanja registrskih tablic. Povprečne stopnje uspešnega raz­poznavanja pri uporabi lokalnega nabora podatkov so dosegle 79,13%. Zaključek: Izboljšanje stopnje razpoznavanja je mogoče doseči, če obstajata dva vira informacij, še posebej na registrskih tablicah, na katerih sta dve neodvisni besedili.
Keywords:registrske tablice, prepoznavanje registrskih tablic, analiza slike, verjetnostno modeliranje, obdelava signalov, cestni promet


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