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Development of License Plate Recognition on Complex Scene with Plate-Style Classification and Confidence Scoring Based on KNN

Vince Jebryl MONTERO, Yong-Jin JEONG

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Summary :

This paper presents an approach for developing an algorithm for automatic license plate recognition system (ALPR) on complex scenes. A plate-style classification method is also proposed in this paper to address the inherent challenges for ALPR in a system that uses multiple plate-styles (e.g., different fonts, multiple plate lay-out, variations in character sequences) which is the case in the current Philippine license plate system. Methods are proposed for each ALPR module: plate detection, character segmentation, and character recognition. K-nearest neighbor (KNN) is used as a classifier for character recognition together with a proposed confidence scoring to rate the decision made by the classifier. A small dataset of Philippine license plates but with relevant features of complex scenarios for ALPR is prepared. Using the proposed system on the prepared dataset, the performance of the system is evaluated on different categories of complex scenes. The proposed algorithm structure shows promising results and yielded an overall accuracy higher than the existing ALPR systems on the dataset consisting mostly of complex scenes.

Publication
IEICE TRANSACTIONS on Information Vol.E101-D No.12 pp.3181-3189
Publication Date
2018/12/01
Publicized
2018/08/24
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDP7060
Type of Manuscript
PAPER
Category
Image Recognition, Computer Vision

Authors

Vince Jebryl MONTERO
  Kwangwoon University
Yong-Jin JEONG
  Kwangwoon University

Keyword