Performance evaluation 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. Previous researches implemented ANPR system on personal computer (PC) with high resolution camera and high computational capability. On the other hand, not...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
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Institute of Advanced Engineering and Science
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/57754/1/GunawanANPRdevelopment_7736-8390-1-Published.pdf http://irep.iium.edu.my/57754/7/Performance%20evaluation%20of%20automatic%20number%20plate%20recognition%20on%20android%20smartphone%20platform.pdf http://irep.iium.edu.my/57754/ http://www.iaescore.com/journals/index.php/IJECE/article/view/7736 |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English |
| Summary: | Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. 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 and implementation of ANPR in smartphone platforms which has limited camera resolution and processing speed. In this paper, various steps to optimize ANPR, including pre-processing, segmentation, and optical character recognition (OCR) using artificial neural network (ANN) and template matching, were described. The proposed ANPR algorithm was based on Tesseract and Leptonica libraries. For comparison purpose, the template matching based OCR will be compared to ANN based OCR. Performance of the proposed algorithm was evaluated on the developed Malaysian number plates’ image database captured by smartphone’s camera. Results showed that the accuracy and processing time of the proposed algorithm using template matching was 97.5% and 1.13 seconds, respectively. On the other hand, the traditional algorithm using template matching only obtained 83.7% recognition rate with 0.98 second processing time. It shows that our proposed ANPR algorithm improved the recognition rate with negligible additional processing time. |
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