A live-video automatic number plate recognition (ANPR) system using convolutional neural network (CNN) with data labelling on an android smartphone
Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, acce...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IJETAE Publication House
2021
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| Online Access: | http://eprints.utem.edu.my/id/eprint/26825/2/IJETAE_1021_11.PDF http://eprints.utem.edu.my/id/eprint/26825/ https://www.ijetae.com/files/Volume11Issue10/IJETAE_1021_11.pdf |
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| Summary: | Automatic Number Plate Recognition (ANPR) combines electronic hardware and complex computer vision software algorithms to recognize the characters on vehicle license plate numbers. Many researchers have proposed and implemented ANPR for various applications such as law enforcement and security, access control, border access, tracking stolen vehicles, tracking traffic violations, and parking management system. This paper discusses a live-video
ANPR system using CNN developed on an Android smartphone embedded with a camera with limited resolution and limited processing power based on Malaysian license plate standards. In terms of system performance, in an ideal outdoor environment with good lighting and direct or slightly skewed camera angle, the recognition works perfectly with a computational time of 0.635 seconds. However, this performance is affected by poor lighting, extremely skewed angle of license plates, and fast vehicle movement. |
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