A robust automated system for detecting and recognising the digit of electrical energy consumption number of the postpaid kWh-meter
Article Sidebar
Main Article Content
Abstract
Most of the processes of kilowatt-hour meter (kWh-meter) reading in Indonesia are still in manual process which may lead to some problems, such as time consumption and high possibility of data entry errors.  Therefore, this study proposes an automated system to minimise these problems. This system is developed for the image with uneven illumination condition and tilted position of stand kWh-meter due to the unavoidable situation while capturing the kWh-meter image.  In this study, the illumination problem is solved by local thresholding and the tilted position of stand kWh-meter is solved by combination of morphology operations and vertical edge detection on the location detection process and vertical-horizontal projections on the segmentation process.  Finally, the numeral recognition is performed by support vector machine (SVM) classifier with zonal density feature as a selected input.  The results show that the accuracy of proposed system is 93.55% on detection location process, 89.38% on segmentation process, and 78.10% on numeral recognition process.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
2. M. Setiawan, “Teknik Pengendalian Kualitas Pembacaan Angka kWh-meter Menggunakan I-MR (Individual-Moving Range) Control Chart,†Lampung, 2010.
3. A. E. Shaputra and A. Nugraha, “Sistem Kerja Automatic Meter Reading dengan Menggunakan Media Power Line Carrier 220V di Kawasan Pondok Indah,†Jakarta, 2011.
4. D. Pramaharsi, “Penanganan Teks ‘PERIKSA’ pada kWh-meter Prabayar di PT. PLN (Persero) Rayon Kota Yogyakarta,†Universitas Gadjah Mada, 2013.
5. PT. PLN, “Term Of Reference (TOR) Pekerjaan Jasa Manajemen Billing PT. PLN Distribusi Jawa Tengah dan D.I. Yogyakarta Tahun 2013 s/d 2018.†Semarang, 2013.
6. C. J. Lakshmi, D. A. J. Rani, D. K. S. Ramakrishna, and M. KantiKiran, “A Novel Approach for Indian License Plate Recognition System,†Int. J. Adv. Eng. Sci. Technol., vol. 6, no. 1, pp. 10–14, 2011.
7. R. Gunawan, S. Suwarno, and W. Hapsari, “Penerapan Optical Character Recognition (OCR) untuk Pembacaan Meteran Listrik PLN,†INFORMATIKA, vol. 10, no. 2, pp. 127–134, 2014.
8. A. Sudiarso and R. J. Merischaputri, “An Automation of Electricity Usage Reading on Postpaid kWh Meter using Kohonen-Type Artificial Neural Network,†Int. J. Mining, Metall. Mech. Eng., vol. 1, no. 4, pp. 238–240, 2013.
9. R. J. Merischaputri, “Otomasi Pembacaan Data Penggunaan Listrik pada kWh-meter Pascabayar untuk Mengurangi Waktu Proses Pengambilan Data Menggunakan Pendekatan Jaringan Syaraf Tiruan Bertipe Kohonen,†Universitas Gadjah Mada, 2013.
10. R. J. Merischaputri, “Otomasi Pembacaan Data Penggunaan Listrik pada kWh Meter Pascabayar untuk Mengurangi Waktu Proses Pengambilan Data dan Perhitungan Tagihan Listrik Menggunakan Pendekatan Jaringan Syaraf Tiruan,†Universitas Gadjah Mada, 2014.
11. J. Sauvola and M. Pietikäinen, “Adaptive document image binarization,†Pattern Recognit., vol. 33, no. 2, pp. 225–236, 2000.
12. Y. Yang and H. Yan, “An Adaptive Logical Method for Binarization of Degraded Document Images,†Pattern Recognit., vol. 33, no. 2000, pp. 787–807, 2000.
13. N. Ntogas and D. Veintzas, “A binarization algorithm for historical manuscripts,†in Proceedings of the 12th WSEAS International Conference on Communications, 2008, pp. 41–51.
14. Y. Qiu, M. Sun, and W. Zhou, “License Plate Extraction Based on Vertical Edge Detection and Mathematical Morphology,†in 2009 International Conference on Computational Intelligence and Software Engineering, 2009, pp. 1–5.
15. A. Wang, X. Liu, Y. Han, and C. Qi, “License Plate Location Algorithm Based on Edge Detection and Morphology,†2012 7th International Forum on Strategic Technology (IFOST). pp. 1–4, 2012.
16. T. S. Rajashree and T. K. Renugha, “Vehicle License Plate Detection using Vertical Edge Detection,†Int. J. Eng. Res. Technol., vol. 3, no. 10, pp. 1225–1232, 2014.
17. B. Enyedi, L. Konyha, C. Szombathy, and K. Fazekas, “Strategies for fast license plate number localization,†in Proceedings Elmar - International Symposium Electronics in Marine, 2004, pp. 579–584.
18. R. Chen and Y. Luo, “An Improved License Plate Location Method Based On Edge Detection,†in Physics Procedia, 2012, vol. 24, pp. 1350–1356.
19. C. C. Lin and W. H. Huang, “Locating License Plate Based on Edge Features of Intensity and Saturation Subimages,†Second Int. Conf. Innov. Comput. Inf. Control. ICICIC 2007, vol. 2, no. 59, pp. 2–5, 2008.
20. J. Jagannathan, A. Sherajdheen, R. M. Vijay Deepak, and N. Krishnan, “License Plate Character Segmentation Using Horizontal and Vertical Projection with Dynamic Thresholding,†in 2013 IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology, ICE-CCN 2013, 2013, pp. 700–705.
21. K. Parasuraman and P. V. Kumar, “An Efficient Method for Indian Vehicle License Plate Extraction and Character Segmentation,†in IEEE International Conference on Computational Intelligence and Computing Research, 2010.
22. B. Santosa, “Tutorial Support Vector Machine.†Surabaya, pp. 1–23, 2005.
23. K. Singh Siddharth, R. Dhir, and R. Rani, “Handwritten Gurmukhi Numeral Recognition using Different Feature Sets,†Int. J. Comput. Appl., vol. 28, no. 2, pp. 20–24, 2011.
24. H. Pujiharsono, H. A. Nugroho, and O. Wahyunggoro, “The Stand Meter Extraction of kWh-meter,†2015 International Conference on Science in Information Technology (ICSITech). pp. 202–206, 2015.
25. R. Fisher, S. Perkins, A. Walker, and E. Wolfart, “Adaptive Thresholding,†Hypermedia Image Processing Reference (HIPR), 2003. [Online]. Available: homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm. [Accessed: 23-Mar-2015].
26. D. Putra, “Penskalaan,†in Pengolahan Citra Digital, Yogyakarta: Penerbit ANDI, 2010, pp. 159–162.
27. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A Practical Guide to Support Vector Classification,†National Taiwan University. 2010.
28. J. C. Platt, “Fast Training of Support Vector Machines Using Sequential Minimal Optimization,†in Advances in Kernel Methods, 1998, pp. 41–65.
29. C.-W. Hsu and C.-J. Lin, “A Comparison of Methods for Multiclass Support Vector Machines,†IEEE Transactions on Neural Networks, vol. 13, no. 2. pp. 415–425, 2002.