Design of automatic number plate recognition on android smartphone platform
Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. It is a combination of hardware and software designed to offer the optimum reliability. Since the past decades, many researchers have been proposed to rec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Engineering and Science
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/56227/ http://irep.iium.edu.my/56227/ http://irep.iium.edu.my/56227/1/Design%20ANPR%20on%20Android_Gunawan2016v3.pdf http://irep.iium.edu.my/56227/7/56227_design%20of%20automatic%20plate_scopus.pdf |
| Summary: | Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to
recognize the character on vehicle number plate. It is a combination of hardware and software designed to
offer the optimum reliability. Since the past decades, many researchers have been proposed to recognize
the vehicle number plate and implemented it in various access control, law enforcement and security,
including parking management system, toll gate access, border access, tracking of stolen vehicles and traffic
violations (speed trap, illegal parking, etc). However, previous researches implemented ANPR system on
personal computer (PC) with high resolution camera and high computational capability. On the other hand,
not many researches have been conducted on the design of ANPR in Android smartphone platform which
has limited camera resolution and limited computational power. The main challenges of implementation
ANPR algorithm on smartphone are higher coding efficiency, lower computational complexity, and higher
the scalability. The objectives of this research is to design ANPR on Android smartphone, including graphical
user interface (GUI) design, process design, and database design. First, a comprehensive survey on the
pre-processing, segmentation, and optical character recognition is conducted. Secondly, proposed system
development and algorithm implementation is explained in more details. Results show that our proposed
design can be implemented effectively in Android smartphone platform. |
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