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

Vince Jebryl MONTERO  Yong-Jin JEONG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.12   pp.3181-3189
Publication Date: 2018/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7060
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
Keyword: 
plate detection,  segmentation,  character recognition,  K-nearest neighbors (KNN),  

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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.