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.

An Approach to License Plate Recognition in Real Time Using Multi-stage Computational Intelligence Classifier

“…Cascade structure with AdaBoost learning was used for LPR in [11]. Application of k-nearest neighbors method for LPR was described in [12].…”

Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks

“…Authors in [38] proposed a mask R-CNN detector for LPs with English and Arabic characters from USA and Tunisia. In [39] Korean and English LPs were targeted, using the term multi-style detection to refer to different country, language and one or two-line LP styles. Most of the reported researches studied the LP Classification (LPC) problem inside the LPD stage.…”

“…In [33] the classification of detected LPs by the issuing country was studied, reporting a classification accuracy of 92.8%. On the other hand, authors in [39] proposed a module to classify the detected LPs to single and double-line, without reporting its accuracy but only the entire system results.…”