A character segmentation method to increase character recognition accuracy for Turkish license plates

Basit Öğe Kaydı
dc.authorid0000-0002-4875-4800
dc.contributor.authorÇavdaroğlu, Gülsüm Çiğdemen_US
dc.contributor.authorGökmen, Mehmeten_US
dc.date.accessioned2026-01-29T08:26:56Z
dc.date.available2026-01-29T08:26:56Z
dc.date.issued2021-12-31
dc.departmentIşık Üniversitesi, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Enformasyon Teknolojileri Bölümüen_US
dc.departmentIşık University, Faculty of Economics, Administrative and Social Sciences, Department of Information Technologiesen_US
dc.description.abstractAutomatic License Plate Recognition is a computer vision technology that provides a way to recognize the vehicle's license plates without direct human intervention. Developing Automatic License Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Automatic License Plate Recognition systems include image acquisition and character segmentation phases. Although there are many studies, the research in character segmentation and improving recognition accuracy remains limited. The lack of an international standard for license plates and the misinterpretation of ambiguous characters are challenging problems for Automatic License Plate Recognition systems. Several academic works have shown that the ambiguous character problem can be overcome by using a second model that contains only these characters. In this study, we propose a new methodology to reduce the character recognition errors of Automatic License Plate Recognition systems. One of the reasons for the low accuracy rates is the problem of ambiguous characters. In most studies using OCR, it was observed that a single model was used for alphanumeric characters during the recognition phase. Instead of using a single model, using separate models for letters and digits will improve the recognition process and increase accuracy. Therefore, we determined whether the characters are letters or numbers, and we expressed the license plates in the form of letters - digits. The method suggested for segmenting blobs worked with an accuracy of 96.12% on the test dataset. The method recommended for generating letter-digit expressions for the license plates worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish license plates. In future studies, we will expand our method by using the license plate dataset of a different country.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÇavdaroğlu, G. Ç. & Gökmen, M. (2021). A character segmentation method to increase character recognition accuracy for Turkish license plates. Mathematics and Computer Science, 6(6), 92-104. doi:https://dx.doi.org/10.11648/j.mcs.20210606.13en_US
dc.identifier.endpage104
dc.identifier.issn2575-6036
dc.identifier.issn2575-6028
dc.identifier.issue6
dc.identifier.startpage92
dc.identifier.urihttps://hdl.handle.net/11729/6957
dc.identifier.urihttps://dx.doi.org/10.11648/j.mcs.20210606.13
dc.identifier.volume6
dc.institutionauthorÇavdaroğlu, Gülsüm Çiğdemen_US
dc.institutionauthorid0000-0002-4875-4800
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherScience Publishing Groupen_US
dc.relation.ispartofMathematics and Computer Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLicense plate recognitionen_US
dc.subjectCharacter segmentationen_US
dc.subjectOptical character recognitionen_US
dc.subjectLetter-digit expressionen_US
dc.subjectImage processingen_US
dc.titleA character segmentation method to increase character recognition accuracy for Turkish license platesen_US
dc.typeArticleen_US
dspace.entity.typePublicationen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
A_Character_Segmentation_Method_to_Increase_Character_Recognition_Accuracy_for_Turkish_License_Plates.pdf
Boyut:
791.05 KB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Makale Koleksiyonu | Enformasyon Teknolojileri Bölümü