Recognition of Hybrid Graphic-Text License Plates
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Published:2021-07-20
Issue:4
Volume:25
Page:416-422
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ISSN:1883-8014
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Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
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language:en
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Short-container-title:JACIII
Author:
Jose John Anthony C., ,Brillantes Allysa Kate M.,Dadios Elmer P.,Sybingco Edwin,Lim Laurence A. Gan,Fillone Alexis M.,Billones Robert Kerwin C.
Abstract
Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.
Funder
Philippine Council for Industry, Energy, and Emerging Technology Research and Development De La Salle University
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
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Cited by
2 articles.
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1. Vehicle Tracking in Low Frame Rate Scenes using Instance Segmentation;2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM);2022-12-01 2. Philippine License Plate Detection on Mobile;2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM);2022-12-01
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