• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Multi-Style License Plate Recognition System using K-Nearest Neighbors


Abstract

There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.


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Cite this article

[IEEE Style]
Soungsill Park, Hyoseok Yoon and Seho Park, "Multi-Style License Plate Recognition System using K-Nearest Neighbors," KSII Transactions on Internet and Information Systems, vol. 13, no. 5, pp. 2509-2528, 2019. DOI: 10.3837/tiis.2019.05.015

[ACM Style]
Park, S., Yoon, H., and Park, S. 2019. Multi-Style License Plate Recognition System using K-Nearest Neighbors. KSII Transactions on Internet and Information Systems, 13, 5, (2019), 2509-2528. DOI: 10.3837/tiis.2019.05.015