For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Development of License Plate Recognition on Complex Scene with Plate-Style Classification and Confidence Scoring Based on KNN
Vince Jebryl MONTERO Yong-Jin JEONG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/12/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
plate detection, segmentation, character recognition, K-nearest neighbors (KNN),
Full Text: PDF(1.3MB)
>>Buy this Article
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.