You are in:Home/Publications/Lamiaa A. Elrefaei, Asrar Bajaber, Sumayyah Natheir, Nada AbuSanab, and Marwa Bazi, “Automatic Electricity Meter Reading Based on Image Processing”, IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT 2015) p. 1- 5,, November 3-5, 2015, Dead Sea, Jordan, DOI: 10.1109/AEECT.2015.7360571.

Prof. lamiaa Elrefaei :: Publications:

Title:
Lamiaa A. Elrefaei, Asrar Bajaber, Sumayyah Natheir, Nada AbuSanab, and Marwa Bazi, “Automatic Electricity Meter Reading Based on Image Processing”, IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT 2015) p. 1- 5,, November 3-5, 2015, Dead Sea, Jordan, DOI: 10.1109/AEECT.2015.7360571.
Authors: Lamiaa A. Elrefaei, Asrar Bajaber ; Sumayyah Natheir ; Nada AbuSanab ; Marwa Bazi
Year: 2015
Keywords: Not Available
Journal: IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT 2015)
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

This paper introduces a system based on image processing to obtain efficiently and accurately reading of the electricity digital meter. In this system the back camera of the mobile phones is used to acquire the image of the electricity meter. The system then applies a sequence of image processing functions to automatically extract and recognize the digits of the meter reading image. This image goes through three main stages: preprocessing which ends up with cropping the numeric reading area, segmentation of individual digits using horizontal and vertical scanning of the cropped numeric area, and recognition of the reading by comparing each segmented digit with the digits templates. The proposed system is implemented using Android Studio software with openCV library and has been tested on 21 images of electric meters captured by Smartphone camera in Saudi Arabia, and results shows a recognition with the accuracy rate of 96,49% (per number digit) and 85.71% accuracy rate for the electricity meter readings. The proposed system will be used in the future to develop a mobile application that could be used by the electricity company employees to facilitate the reading process.

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