TY - GEN
T1 - Video-Based License Plate Recognition Using Single Shot Detector and Recurrent Neural Network
AU - Navastara, Dini Adni
AU - Musyafira, Nuzha
AU - Fatichah, Chastine
AU - Maharani, Safhira
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Each vehicle has its own identity, in other words, the vehicle number plate. This identity often uses in parking processing, security development, and toll systems. It is necessary to develop an automated system that can be used and supported by vehicle number plates known as License Plate Recognition (LPR). This paper proposed the LPR system based on video data CCTV using the Single Shot Detector to localize the license plate, the Connected Component Labeling to do the character segmentation, and Recurrent Neural Network to recognize the characters on the license plate. This study shows our proposed method works well based on the experimental result, with an average accuracy of 94.01 % for license plate localization, 84.08% for character segmentation, and 93.53% for character recognition.
AB - Each vehicle has its own identity, in other words, the vehicle number plate. This identity often uses in parking processing, security development, and toll systems. It is necessary to develop an automated system that can be used and supported by vehicle number plates known as License Plate Recognition (LPR). This paper proposed the LPR system based on video data CCTV using the Single Shot Detector to localize the license plate, the Connected Component Labeling to do the character segmentation, and Recurrent Neural Network to recognize the characters on the license plate. This study shows our proposed method works well based on the experimental result, with an average accuracy of 94.01 % for license plate localization, 84.08% for character segmentation, and 93.53% for character recognition.
KW - Connected Component Labelling
KW - License Plate Recognition
KW - Recurrent Neural Network
KW - Single Shot Detector
UR - http://www.scopus.com/inward/record.url?scp=85123309601&partnerID=8YFLogxK
U2 - 10.1109/ICTS52701.2021.9608790
DO - 10.1109/ICTS52701.2021.9608790
M3 - Conference contribution
AN - SCOPUS:85123309601
T3 - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
SP - 151
EP - 154
BT - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information and Communication Technology and System, ICTS 2021
Y2 - 20 October 2021 through 21 October 2021
ER -