Vehicle License Plate Detection and Recognition using OpenCV and Tesseract OCR

Kalaphath Kounlaxay (1), Yeo Chan Yoon (2), Soo Kyun Kim (3)
(1) Department of Computer Engineering, Souphanouvong University, Louangprabang City, 0600, Laos
(2) Department of Artificial Intelligence, Jeju National University, Jeju City, 63243, Republic of Korea
(3) Department of Computer Engineering, Jeju National University, Jeju City, 63143, Republic of Korea
Fulltext View | Download
How to cite (IJASEIT) :
Kounlaxay, Kalaphath, et al. “Vehicle License Plate Detection and Recognition Using OpenCV and Tesseract OCR”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 4, Aug. 2024, pp. 1170-7, doi:10.18517/ijaseit.14.4.18137.
License plate recognition (LPR) is essential as the number of vehicles increases and the human ability to accomplish this task is limited. If human labor is used to manage these, it will take a lot of time and energy and cause a discrepancy. License Plate Recognition (LPR) is an advanced technology that leverages optical character recognition (OCR) and various image processing methods to read vehicle license plates automatically. Typically, an LPR system comprises two primary components: detecting vehicles and their license plates and recognizing the alphanumeric characters displayed on those plates. This study explores the use of OpenCV for license plate detection and Tesseract OCR for character recognition. In this research, the dataset for training and testing the system included 100 license plates evenly split between plates featuring English and Lao characters. The Lao license plates presented unique complexities due to their specific characteristics. The experimental setup involved processing images of license plates taken from multiple angles. The system's performance was evaluated based on the speed and accuracy of line and character recognition. For English character plates, the recognition process took 0.12 seconds with an accuracy of 98.8%. In contrast, the Lao character plates required 0.24 seconds, achieving an accuracy rate of 89.42%.

A. S. D. Sham, P. Pandey, S. Jain, and S. Kalaivani, “Automatic License Plate Recognition Using YOLOV4 and Tesseract OCR,” International Journal Of Electrical Engineering and Technology, vol. 12, no. 5, May 2021, doi: 10.34218/ijeet.12.5.2021.006.

I. R. Khan et al., “Automatic License Plate Recognition in Real-World Traffic Videos Captured in Unconstrained Environment by a Mobile Camera,” Electronics, vol. 11, no. 9, p. 1408, Apr. 2022, doi:10.3390/electronics11091408.

W. Puarungroj and N. Boonsirisumpun, “Thai License Plate Recognition Based on Deep Learning,” Procedia Computer Science, vol. 135, pp. 214–221, 2018, doi: 10.1016/j.procs.2018.08.168.

S. Parvin, L. J. Rozario, and Md. E. Islam, “Vehicle Number Plate Detection and Recognition Techniques: A Review,” Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 423–438, Mar. 2021, doi: 10.25046/aj060249.

O. Bulan, V. Kozitsky, P. Ramesh, and M. Shreve, “Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2351–2363, Sep. 2017, doi: 10.1109/tits.2016.2639020.

A.I. Khan and S. Al-Habsi, “Machine Learning in Computer Vision,” Procedia Computer Science, vol. 167, pp. 1444–1451, 2020, doi:10.1016/j.procs.2020.03.355.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.

K. L. Masita, A. N. Hasan, and T. Shongwe, “Deep Learning in Object Detection: a Review,” 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Aug. 2020, doi:10.1109/icabcd49160.2020.9183866.

A. Akhil, R. R. Praneeth, and Kumar, “Object Detection/Recognition Using Machine Learning Techniques in AWS,” The International Journal of Analytical And Experimental Modal Analysis, vol. 12, no. 3, 2020.

S. N. Srivatsa, G. Sreevathsa, G. Vinay, and P. Elaiyaraja, “Object Detection using Deep Learning with OpenCV and Python,”, International Research Journal of Engineering and Technology (IRJET), vol. 8, no. 1, pp. 227–230, 2021.

Z. Akhtar and R. Ali, “Automatic Number Plate Recognition Using Random Forest Classifier,” SN Computer Science, vol. 1, no. 3, Apr. 2020, doi: 10.1007/s42979-020-00145-8.

Y. Guan et al., “An Object Detection Framework Based on Deep Features and High-Quality Object Locations,” Traitement du Signal, vol. 38, no. 3, pp. 719–730, Jun. 2021, doi: 10.18280/ts.380319.

R. Padilla, S. L. Netto, and E. A. B. da Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Jul. 2020, doi: 10.1109/iwssip48289.2020.9145130.

Y. Pan and F. Dong, “Suppression and Enhancement of Overlapping Bounding Boxes Scores in Object Detection,” 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec. 2019, doi:10.1109/isspit47144.2019.9001826.

F. Shao et al., “Deep Learning for Weakly-Supervised Object Detection and Localization: A Survey,” Neurocomputing, vol. 496, pp. 192–207, Jul. 2022, doi: 10.1016/j.neucom.2022.01.095.

X. Wu, D. Sahoo, and S. C. H. Hoi, “Recent advances in deep learning for object detection,” Neurocomputing, vol. 396, pp. 39–64, Jul. 2020, doi: 10.1016/j.neucom.2020.01.085.

W. R. Sania, C. A. Sari, E. H. Rachmawanto, and M. Doheir, “Bounding Box and Thresholding in Optical Character Recognition for Car License Plate Recognition,” sinkron, vol. 8, no. 4, Oct. 2023, doi: 10.33395/sinkron.v8i4.12944.

L. Liu, Y. Wang, and W. Chi, “Image Recognition Technology Based on Machine Learning,” IEEE Access, pp. 1–1, 2024, doi:10.1109/access.2020.3021590.

M. Aamir, “A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition,” Computers, Materials & Continua, vol. 75, no. 3, pp. 6351–6373, 2023, doi:10.32604/cmc.2023.038173.

S. Bouraya, and A. Belanngour, “Object Detectors’ Convolutional Neural Networks backbones : a review and a comparative study,” International Journal of Emerging Trends in Engineering Research, vol. 9, no. 11, pp. 1379–1386, Nov. 2021, doi:10.30534/ijeter/2021/039112021.

R. L. Galvez, A. A. Bandala, E. P. Dadios, R. R. P. Vicerra, and J. M. Z. Maningo, “Object Detection Using Convolutional Neural Networks,” TENCON 2018 - 2018 IEEE Region 10 Conference, Oct. 2018, doi: 10.1109/tencon.2018.8650517.

J. Ren, and Yi, W. Shim, “Overview of Object Detection Algorithms Using Convolutional Neural Networks,” Journal of Computer and Communications, vol. 10, no. 1, pp. 115–132, 2022.

A. Kumar and S. Srivastava, “Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector,” Procedia Computer Science, vol. 171, pp. 2610–2617, 2020, doi: 10.1016/j.procs.2020.04.283.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2014, doi: 10.1109/cvpr.2014.81.

G. M. G. Madhuri, B. Shaik, S. S. Tungala, CH. D. D. S. Kumar, and S. V. R. Pilla, “Recognition and Tracing of Object Using CNN,” Journal of Engineering Sciences, vol. 14, no. 03, pp. 589-593, 2023.

R. TH. Hasan and A. . Bibo Sallow, “Face Detection and Recognition Using OpenCV”, jscdm, vol. 2, no. 2, pp. 86–97, Oct. 2021.

M. K. Hossen, "Application of Python-OpenCV to detect contour of shapes and colour of a real image", International Journal of Novel Research in Computer Science and Software Engineering, vol. 9, no. 2, pp. 20-25, 2022.

F. Yu, J. Shuai, B. Yin, and X. Feng, “3D Depth of Field Acquisition Based on OpenCV,” Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications, 2016, doi: 10.2991/wartia-16.2016.195.

A. Shihab, Z. S. Noori, and M. A. Jasim, “Dynamic Object Detection and Evaluation Patterns using OpenCV,” International Refereed Journal of Reviews and Research, vol. 10, no. 1, 2022.

Z. N. Khudhair et al., “Color to Grayscale Image Conversion Based on Singular Value Decomposition,” IEEE Access, vol. 11, pp. 54629–54638, 2023, doi: 10.1109/access.2023.3279734.

M. S. M. Rahim, A. Norouzi, A. Rehman and T. Saba, "3D bones segmentation based on ct images visualization", Biomed. Res., vol. 28, no. 8, pp. 3641-3644, 2017.

J. Chen, B. Guan, H. Wang, X. Zhang, Y. Tang, and W. Hu, “Image Thresholding Segmentation Based on Two Dimensional Histogram Using Gray Level and Local Entropy Information,” IEEE Access, vol. 6, pp. 5269–5275, 2018, doi: 10.1109/access.2017.2757528.

Z. Wang, J. Wang, W. Wang, C. Gao, and S. Chen, “A Novel Thresholding Algorithm for Image Deblurring Beyond Nesterov’s Rule,” IEEE Access, vol. 6, pp. 58119–58131, 2018, doi:10.1109/access.2018.2873628.

J. Shashirangana, H. Padmasiri, D. Meedeniya, and C. Perera, “Automated License Plate Recognition: A Survey on Methods and Techniques,” IEEE Access, vol. 9, pp. 11203–11225, 2021, doi:10.1109/access.2020.3047929.

H. Li, P. Wang, and C. Shen, “Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1126–1136, Mar. 2019, doi: 10.1109/tits.2018.2847291.

X. T. Nguyen, K.-T. Nguyen, H.-J. Lee, and H. Kim, “ROI-Based LiDAR Sampling Algorithm in on-Road Environment for Autonomous Driving,” IEEE Access, vol. 7, pp. 90243–90253, 2019, doi: 10.1109/access.2019.2927036.

Y. Wang, X. Zheng, and N. Gao, “A Region of Interest-Based Electrophysiological Source Imaging Technology and its Applications in Analysis of Motor Imagery EEG Signals,” IEEE Access, vol. 11, pp. 140596–140608, 2023, doi:10.1109/access.2023.3339857.

M. Samantaray, A. K. Biswal, D. Singh, D. Samanta, M. Karuppiah, and N. P. Joseph, “Optical Character Recognition (OCR) based Vehicle’s License Plate Recognition System Using Python and OpenCV,” 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Dec. 2021, doi:10.1109/iceca52323.2021.9676015.

C. Adjetey and K. S. Adu-Manu, “Content-based Image Retrieval using Tesseract OCR Engine and Levenshtein Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, 2021, doi:10.14569/ijacsa.2021.0120776.

S. Bansal, M. Gupta, and A. K. Tyagi, “A Necessary Review on Optical Character Recognition (OCR) System for Vehicular Applications,” 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Jul. 2020, doi:10.1109/icirca48905.2020.9183330.

T. M. Breuel, A. Ul-Hasan, M. A. Al-Azawi, and F. Shafait, “High-Performance OCR for Printed English and Fraktur Using LSTM Networks,” 2013 12th International Conference on Document Analysis and Recognition, Aug. 2013, doi: 10.1109/icdar.2013.140.

X. F. Wang, Z.-H. He, K. Wang, Y.-F. Wang, L. Zou, and Z.-Z. Wu, “A survey of text detection and recognition algorithms based on deep learning technology,” Neurocomputing, vol. 556, p. 126702, Nov. 2023, doi: 10.1016/j.neucom.2023.126702.

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).