Version 1
: Received: 15 April 2021 / Approved: 16 April 2021 / Online: 16 April 2021 (11:11:54 CEST)
How to cite:
Çavdaroğlu, G.Ç.; Gökmen, M. A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates. Preprints2021, 2021040440. https://doi.org/10.20944/preprints202104.0440.v1
Çavdaroğlu, G.Ç.; Gökmen, M. A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates. Preprints 2021, 2021040440. https://doi.org/10.20944/preprints202104.0440.v1
Çavdaroğlu, G.Ç.; Gökmen, M. A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates. Preprints2021, 2021040440. https://doi.org/10.20944/preprints202104.0440.v1
APA Style
Çavdaroğlu, G.Ç., & Gökmen, M. (2021). A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates. Preprints. https://doi.org/10.20944/preprints202104.0440.v1
Chicago/Turabian Style
Çavdaroğlu, G.Ç. and Mehmet Gökmen. 2021 "A Character Segmentation Method to Increase Character Recognition Accuracy for Turkish Number Plates" Preprints. https://doi.org/10.20944/preprints202104.0440.v1
Abstract
Automatic Number Plate Recognition is a computer vision technology that provides a way to recognize the vehicles number plates without direct human intervention. Developing Automatic Number Plate Recognition methodologies is a widely studied topic among the computer vision community to increase the accuracy rates. Although there are many studies, the research in character segmentation and improving the recognition accuracy remains limited. In this study, a new methodology is proposed to reduce the character recognition errors of Automatic Number Plate Recognition systems. To achieve this, it will be determined whether the characters are letters or numbers, and the number plates will be expressed in the form of letters - digit. The method suggested for segmenting blobs correctly worked with an accuracy of 96.12% on the test dataset. The method suggested for generating letter-digit expression for the number plates correctly worked with an accuracy of 99.28% on the test dataset. The proposed methodology can work only on Turkish number plates. In the future studies, the proposed methodology can be expanded by using the number plate dataset of a different country.
Keywords
license plate recognition; character segmentation; optical character recognition
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
