Bounding Box and Thresholding in Optical Character Recognition for Car License Plate Recognition

Authors

  • Wulida Rizki Sania University of Dian Nuswantoro
  • Christy Atika Sari University of Dian Nuswantoro
  • Eko Hari Rachmawanto University of Dian Nuswantoro
  • Mohamed Doheir University Technical Malaysia Melacca

DOI:

10.33395/sinkron.v8i4.12944

Keywords:

Bounding Box, License Plate Recognition, Optical Character Recognition (OCR), Template matching, Thresholding

Abstract

License plate recognition plays a central role in a variety of application contexts, including traffic management, automated parking, and law enforcement. Among the various approaches available, the Optical Character Recognition (OCR) technique has proven its effectiveness in recognizing characters in license plate images. This study describes an approach for detecting and recognizing vehicle license plates by utilizing the OCR method with Bounding Box, Thresholding, and template matching. In addition, this study uses MATLAB R2022a software as the main tool in developing and implementing the method. The goal is to recognize vehicle license plates from images, describe their characteristics, and generate relevant information. This approach involves a series of image processing steps starting with the pre-processing stage, followed by the process of binarization and license plate segmentation. After successfully isolating the license plate area, isolating the character using a bounding box is performed using image separation techniques. The OCR method is used to recognize license plate characters through comparison using the correlation method. Through a series of experiments on several image datasets, this approach succeeded in showing that out of 20 sampled license plate images, the results obtained were a reading accuracy of 93.55% of 100%, recognizing 13 out of 20 license plate images accurately when tested. Thus, the findings of this research are expected to contribute to the recognition of vehicle license plates that are accurate and efficient, by utilizing image processing techniques and OCR methods implemented using MATLAB R2022a software.

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Author Biographies

Christy Atika Sari, University of Dian Nuswantoro

I received the master in Informatic Engineering from Dian Nuswantoro University and University Teknikal Malaysia Melaka (UTeM) in 2012. She is currently active as author in international journal and confrence scopus indexed. She also awarded as best author and best paper in national and intenational confrence in 2019 and 2020 respectively and awarded from Ristekbrin DIKTI as the indonesian top 50 best researchers in 2020. She was served as editor in several Indonesian Journal and reviewer both of national and international journal. She currently as lecturer in intelligent system and continue to develop the research field iimage processing, machine learning, deep learning and data hiding. 
Scopus : https://www.scopus.com/authid/detail.uri?authorId=57193848115
Scholar : https://scholar.google.com/citations?user=TKVTFhIAAAAJ&hl=en&oi=ao

Eko Hari Rachmawanto, University of Dian Nuswantoro

Eko Hari Rachmawanto, M.Kom, M.CS
Head of Study Program of Informatics Engineering (S1)
University of Dian Nuswantoro (PSDKU Kediri)
Penanggungan 41A, Bandar Lor, Mojoroto, Kediri, 64114, Indonesia

Scopus : https://www.scopus.com/authid/detail.uri?authorId=57193850466
Scholar : https://scholar.google.com/citations?user=RG2Im6cAAAAJ&hl=en
Orcid :  https://orcid.org/0000-0001-6014-1903

Mohamed Doheir, University Technical Malaysia Melacca

Mohamed Doheir received his doctor in Healthcare Management in 2020 form
University Teknikal Malaysia Melaka (UTeM). He is received his master from University
Teknikal Malaysia Melaka (UTeM) in 2012. His current research such as Cloud Computing,
Information Technology, System Management. Now, he served as lecturer in University Technical Malaysia Melacca (UTeM)

References

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Etomi, E. E., & Onyishi, D. U. (2021). Automated number plate recognition system. 2(1), 38–48. https://doi.org/10.47524/tjst.21.6

Gnanaprakash, V., Kanthimathi, N., & Saranya, N. (2021). Automatic number plate recognition using deep learning. IOP Conference Series: Materials Science and Engineering, 1084(1), 012027. https://doi.org/10.1088/1757-899x/1084/1/012027

Hamdoun, N., & Mentagui, D. (2022). Image Processing in Automatic License Plate Recognition Using Combined Methods. Serdica Journal of Computing, 16(1), 1–23. https://doi.org/10.55630/sjc.2022.16.1-23

Huang, Q., Cai, Z., & Lan, T. (2021). A Single Neural Network for Mixed-Style License Plate Detection and Recognition. IEEE Access, 9, 21777–21785. https://doi.org/10.1109/ACCESS.2021.3055243

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Kusumadewi, I., Sari, C. A., Moses Setiadi, D. R. I., & Rachmawanto, E. H. (2019). License Number Plate Recognition using Template Matching and Bounding Box Method. Journal of Physics: Conference Series, 1201(1). https://doi.org/10.1088/1742-6596/1201/1/012067

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Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020). Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542

Selmi, Z., Halima, M. Ben, Pal, U., & Alimi, M. A. (2020). DELP-DAR system for license plate detection and recognition. Pattern Recognition Letters, 129, 213–223. https://doi.org/10.1016/j.patrec.2019.11.007

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Vaishnav, A., & Mandot, M. (2020). Template Matching for Automatic Number Plate Recognition System with Optical Character Recognition. In Advances in Intelligent Systems and Computing (Vol. 933). Springer Singapore. https://doi.org/10.1007/978-981-13-7166-0_69

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Agrawal, R., Agarwal, M., & Krishnamurthi, R. (2020). Cognitive Number Plate Recognition using Machine Learning and Data Visualization Techniques. 2020 6th International Conference on Signal Processing and Communication, ICSC 2020, 101–107. https://doi.org/10.1109/ICSC48311.2020.9182744

Etomi, E. E., & Onyishi, D. U. (2021). Automated number plate recognition system. 2(1), 38–48. https://doi.org/10.47524/tjst.21.6

Gnanaprakash, V., Kanthimathi, N., & Saranya, N. (2021). Automatic number plate recognition using deep learning. IOP Conference Series: Materials Science and Engineering, 1084(1), 012027. https://doi.org/10.1088/1757-899x/1084/1/012027

Hamdoun, N., & Mentagui, D. (2022). Image Processing in Automatic License Plate Recognition Using Combined Methods. Serdica Journal of Computing, 16(1), 1–23. https://doi.org/10.55630/sjc.2022.16.1-23

Huang, Q., Cai, Z., & Lan, T. (2021). A Single Neural Network for Mixed Style License Plate Detection and Recognition. IEEE Access, 9, 21777–21785. https://doi.org/10.1109/ACCESS.2021.3055243

Humeau-Heurtier, A. (2019). Texture feature extraction methods: A survey. IEEE Access, 7, 8975–9000. https://doi.org/10.1109/ACCESS.2018.2890743

Jamtsho, Y., Riyamongkol, P., & Waranusast, R. (2020). Real-time Bhutanese license plate localization using YOLO. ICT Express, 6(2), 121–124. https://doi.org/10.1016/j.icte.2019.11.001

Kusumadewi, I., Sari, C. A., Moses Setiadi, D. R. I., & Rachmawanto, E. H. (2019). License Number Plate Recognition using Template Matching and Bounding Box Method. Journal of Physics: Conference Series, 1201(1). https://doi.org/10.1088/1742-6596/1201/1/012067

Lin, G., Xue, B., Xu, B., & Chen, C. (2019). License plate recognition based on mathematical morphology and template matching. Proceedings - 2019 Chinese Automation Congress, CAC 2019, 405–410. https://doi.org/10.1109/CAC48633.2019.8996973

Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020). Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR). IEEE Access, 8, 142642–142668. https://doi.org/10.1109/ACCESS.2020.3012542

Selmi, Z., Halima, M. Ben, Pal, U., & Alimi, M. A. (2020). DELP-DAR system for license plate detection and recognition. Pattern Recognition Letters, 129, 213–223. https://doi.org/10.1016/j.patrec.2019.11.007

Shashidhar, R., Manjunath, A. S., Santhosh Kumar, R., Roopa, M., & Puneeth, S. B. (2021). Vehicle Number Plate Detection and Recognition using YOLO- V3 and OCR Method. 2021 IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2021. https://doi.org/10.1109/ICMNWC52512.2021.9688407

Vaishnav, A., & Mandot, M. (2020). Template Matching for Automatic Number Plate Recognition System with Optical Character Recognition. In Advances in Intelligent Systems and Computing (Vol. 933). Springer Singapore. https://doi.org/10.1007/978-981-13-7166-0_69

Wu, F., Zhu, C., Xu, J., Bhatt, M. W., & Sharma, A. (2022). Research on image text recognition based on canny edge detection algorithm and k-means algorithm. International Journal of System Assurance Engineering and Management, 13(s1), 72–80. https://doi.org/10.1007/s13198-021-01262-0

Zhang, L., Wang, P., Li, H., Li, Z., Shen, C., & Zhang, Y. (2021). A Robust Attentional Framework for License Plate Recognition in the Wild. IEEE Transactions on Intelligent Transportation Systems, 22(11), 6967–6976. https://doi.org/10.1109/TITS.2020.3000072

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How to Cite

Sania, W. R., Sari, C. A., Rachmawanto, E. H., & Doheir, M. (2023). Bounding Box and Thresholding in Optical Character Recognition for Car License Plate Recognition. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2716-2726. https://doi.org/10.33395/sinkron.v8i4.12944

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

License

Copyright (c) 2023 Wulida Rizki Sania, Christy Atika Sari, Eko Hari Rachmawanto, Mohamed Doheir

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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