Volume : 2, Issue : 4, APR 2016

MOBILE BASED AUTOMATIC ELECTRICITY BILL GENERATION

Vikrant A. Agaskar, Abhishek D. Singh, Onkar D. Kandalgaonkar, Shilpa S. Wade

Abstract

Today in 21st century the things are changing from old traditional methods to new modern technology. Most of the things are being computerized. But the process of meter reading and generation of bills has not been changed yet. Meter reading and billing are complex tasks of electricity, water and gas supplier companies. The current technology of billing process uses manual process of meter reading, updating the server with reading and billing customer. We have planned to implement a technology that includes android application and web application to get reading, updating server and inform consumers about bill units and amount. Android application we develop will be used to get the readings from the meter automatically by simply capturing the image of the meter. The customer will receive a mail regarding the bill as soon as the bill is generated. With the help of web application customer can view his bill. For building our project, we have used Android studio which supports Android Software Development Kit. To get readings from the image, OCR(optical Character Recognition) is used. We are using Tesseract OCR engine for it.

Keywords

Optical Character Recognition , OCR , Tesseract , electricity bill.

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References

1. Chirag Patel, Atul patel, Dharmendra patel, Optical Character Recognition by Open
Source OCR Tool Tesseract: A Case Study, International Journal of Computer Applications
(0975 –8887)Volume 55–No.10, October 2012
2. S.V. Rice, F.R. Jenkins, T.A. Nartker, The Fourth Annual Test of OCR Accuracy, Technical
Report 95-03, Information Science Research Institute, University of Nevada, Las
Vegas, July 1995
3. SMITH, R.2007.An Overview of the Tesseract OCR Engine. In proceedings of Document
analysis and Recognition. ICDAR 2007. IEEE Ninth International Conference.
4. Umapada Pal, Partha Pratim Roy, Nilamadhaba Tripathy, Josep Lladós. December
2010.Multioriented Bangla and Devnagari text recognition, Pattern Recognition, Volume
43,Issue12,Pages 4124-4136, 10.1016/j.patcog.2010.06.017.
5. R.W. Smith, The Extraction and Recognition of Text from Multimedia Document
Images, PhD Thesis, University of Bristol, November 1987.

6. R. Smith, “A Simple and Efficient Skew Detection Algorithm via Text Row Accumulation”,
Proc. of the 3Rd Int. Conf. on Document Analysis and Recognition(Vol. 2), IEEE
1995, pp. 1145-1148.
7. P.J. Rousseeuw, A.M. Leroy, Robust Regression and Outlier Detection, Wiley-IEEE,
2003.
8. S.V. Rice, G. Nagy, T.A. Nartker, Optical Character Recognition: An Illustrated Guide
to the Frontier, Kluwer Academic Publishers, USA 1999, pp. 57-60.