Korean License Plate Recognition System Using Combined Neural Networks

Citations

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1
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SCOPUS

14

초록

We developed a deep learning application to detect and recognize Korean cars' license plates from images. It is an advanced application that targets to provide deep learning solution that can be applied in many areas including Intelligent Transportation System, Internet of Things and Smart City. Despite, there have been many approaches and studies on license plate localization, character segmentation and recognition, there have not been highly demanded results particularly using deep neural networks. Traditional approaches on license plate detection have achieved quite a high accuracy in detection and recognition, in which mostly Optical Character Recognition (OCR) is used. Nevertheless, in this research, we developed our own method that is a combination of scene text recognition technique with Geometrical Image Transformation (GIT) to recognize number plates for combined neural networks and achieving 99.8% and 95.7% of detection and recognition accuracy respectively.

키워드

CNN - Convolutional Neural NetworksYOLOv3-You Only Look OnceRNN - Recurrent Neural NetworksLSTM - Long Short-Term MemoryCRNN - Convolutional Recurrent Neural Networks
제목
Korean License Plate Recognition System Using Combined Neural Networks
저자
Usmankhujaev, SaidrasulLee, SunwooKwon, Jangwoo
DOI
10.1007/978-3-030-23887-2_2
발행일
2020
유형
Proceedings Paper
저널명
DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 16TH INTERNATIONAL CONFERENCE
1003
페이지
10 ~ 17