DOI : https://doi.org/10.18517/ijaseit.14.4.18137

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
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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.
Citation Format :
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%.

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