Penerapan Metode YOLO dan Tesseract-OCR untuk Pendataan Plat Nomor Kendaraan Bermotor Umum di Indonesia Menggunakan Raspberry Pi
(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Teknik Elektro
(*) Corresponding Author
Abstract
Parking system is a common thing to find in public places. Parking system usually comes with a program that enables to detect and read license plates. With the advancement of technology, there are many systems / programs that are able to automatically detect and read license plates, but they come with a costly price.
In this research, Raspberry Pi 4 will be used as the main platform. With the usage of Raspberry Pi, it is expected to reduce the cost needed to achieve the same output. However, by using Raspberry Pi, the hardware specifications are not as good as computer in general. In this research YOLO will be used to detect the license plate and Tesseract-OCR is used to read the characters on the license plate.
From this research, it can be concluded that program can implement YOLO and Tesseract-OCR to detect and read public transportation license plates while being run on Raspberry Pi 4. To get the optimal results, the input image needs to be taken at daytime, using high quality camera, and implement only the necessary pre-processing methods.
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Al-Ameen, Z., Muttar, A., & Al-Badrani, G. 2019. Improving
the Sharpness of Digital Image Using an Amended Unsharp
Mask Filter. International Journal of Image, Graphics and
Signal Processing, 11(3), 1–9.
https://doi.org/10.5815/ijigsp.2019.03.01
Ali, N., Isheawy, M., & Hasan, H. 2015. Optical Character
Recognition (OCR) System. IOSR Journal of Computer
Engineering Ver. II, 17(2), 2278–2661.
https://doi.org/10.9790/0661-17222226
Azam, M. A. 2016. A Review of Various Histogram
Equalization Techniques for Image Enhancement. IOSR
Journal of Electrical and Electronics Engineering, 11(04),
–51. https://doi.org/10.9790/1676-1104044851
Bowman, R. W., Vodenicharski, B., Collins, J. T., & Stirling,
J. 2020. Flat-Field and Colour Correction for the Raspberry Pi
Camera Module. Journal of Open Hardware, 4(1), 1–9.
https://doi.org/10.5334/joh.20
Du, J. 2018. Understanding of Object Detection Based on
CNN Family and YOLO. Journal of Physics: Conference
Series, 1004(1). https://doi.org/10.1088/1742-
/1004/1/012029
Gedraite, E. S., & Hadad, M. 2011. Investigation on the effect
of a Gaussian Blur in image filtering and segmentation.
Proceedings Elmar - International Symposium Electronics in
Marine, January 2011, 393–396.
Kanan, C., & Cottrell, G. W. 2012. Color-to-grayscale: Does
the method matter in image recognition? PLoS ONE, 7(1).
https://doi.org/10.1371/journal.pone.0029740
Mau, S. D. B. 2016. Pengaruh Histogram Equalization Untuk
Perbaikan Kualitas Citra Digital. Simetris : Jurnal Teknik
Mesin, Elektro Dan Ilmu Komputer, 7(1), 177.
https://doi.org/10.24176/simet.v7i1.502
Nayyar, A., & Puri, V. 2015. Raspberry Pi-A Small , Powerful
, Cost Effective and Efficient Form Factor Computer : A
Review International Journal of Advanced Research in
Raspberry Pi- A Small , Powerful , Cost Effective and
Efficient Form Factor Computer : A Review. December.
https://www.researchgate.net/profile/Anand_Nayyar/publicat
ion/305668622_Raspberry_PiA_Small_Powerful_Cost_Effective_and_Efficient_Form_Fa
ctor_Computer_A_Review/links/5798c41908aeb0ffcd08b80f
/Raspberry-Pi-A-Small-Powerful-Cost-Effective-andEfficient-Form
Nurhaida, I., Nududdin, I., & Ramayanti, D. 2020. Indonesian
license plate recognition with improved horizontal-vertical
edge projection. Indonesian Journal of Electrical Engineering
and Computer Science, 21(2), 811–821.
https://doi.org/10.11591/ijeecs.v21.i2.pp811-821
Padilla, R., Netto, S. L., & Da Silva, E. A. B. 2020. A Survey
on Performance Metrics for Object-Detection Algorithms.
International Conference on Systems, Signals, and Image
Processing, 2020-July(July), 237–242.
https://doi.org/10.1109/IWSSIP48289.2020.9145130
Patel, C., Patel, A., & Patel, D. 2012. Optical Character
Recognition by Open source OCR Tool Tesseract: A Case
Study. International Journal of Computer Applications,
(10), 50–56. https://doi.org/10.5120/8794-2784
Porikli, F., & Yilmaz, A. 2012. Object detection and tracking.
In Studies in Computational Intelligence (Vol. 409, Issue
January). https://doi.org/10.1007/978-3-642-28598-1_1
Raid, A. ., Khedr, W. ., El-dosuky, M. ., & Aoud, M. 2014.
Image Restoration Based on Morphological Operations.
International Journal of Computer Science, Engineering and
Information Technology, 4(3), 9–21.
https://doi.org/10.5121/ijcseit.2014.4302
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. 2016.
You only look once: Unified, real-time object detection.
Proceedings of the IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 2016-Decem, 779–
https://doi.org/10.1109/CVPR.2016.91
Villar, S. A., Torcida, S., & Acosta, G. G. 2017. Median
Filtering: A New Insight. Journal of Mathematical Imaging
and Vision, 58(1), 130–146. https://doi.org/10.1007/s10851-
-0694-0
Yousefi, J. 2015. Image Binarization using Otsu Thresholding
Algorithm. Research Gate, May.
https://doi.org/10.13140/RG.2.1.4758.9284
Zaafouri, A., Sayadi, M., & Fnaiech, F. 2011. A developed
unsharp masking method for images contrast enhancement.
International Multi-Conference on Systems, Signals and
Devices, SSD’11 - Summary Proceedings, November 2017.
https://doi.org/10.1109/SSD.2011.5767378
Zhang, S., Hu, Y., & Bian, G. 2017. Research on string
similarity algorithm based on Levenshtein Distance.
Proceedings of 2017 IEEE 2nd Advanced Information
Technology, Electronic and Automation Control Conference,
IAEAC 2017, 1, 2247–2251.
https://doi.org/10.1109/IAEAC.2017.8054419
Zhao, L., & Li, S. 2020. Object detection algorithm based on
improved YOLOv3. Electronics (Switzerland), 9(3).
https://doi.org/10.3390/electronics9030537
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