An Automatic System for Water Meter Index Reading

  • Naim AymanEmail author
  • Abdessadek Aaroud
  • Said Saadani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 912)


Water meter is used as a tool to calculate water consumption. This tool works by utilizing water flow and shows the calculation result with mechanical digit counter. Practically, in everyday use, an operator will manually check the digit counter periodically. The operator makes logs of the number shows by water meter to know the water consumption. This manual operation is time consuming and prone to human error. Therefore, we propose in this article an Android mobile application that calculates the customer’s water consumption in real time. By having a Smartphone supporting applications that run on Android, the customer can access his water bill at any time by creating his own subscriber account. Once the subscriber account has been created, the subscriber can take an image of his water meter which will subsequently be sent to the server for processing, the level of consumption as well as any alerts will be transmitted to the citizen on his Smartphone according to the image sent. The customer will, of course, be connected to the Internet to use this application.


Character recognition Viola Jones method Eigenfaces method 


  1. 1.
    Vanetti, M., Gallo, I., Nodari, A.: GAS meter reading from real world images using a multinet system. Pattern Recogn. Lett. 34, 519 (2013)CrossRefGoogle Scholar
  2. 2.
    Nodari, A., Gallo, I.: A multi-neural network approach to image detection and segmentation of gas meter counter. In: Proceedings of the IAPR Conference on Machine Vision Applications, Nara, Japan (2011)Google Scholar
  3. 3.
    Gallo, I., Zamberletti, A., Noce, L.: Robust angle invariant GAS meter reading. In: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA), p. 1 (2015)Google Scholar
  4. 4.
    Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1480 (2015)CrossRefGoogle Scholar
  5. 5.
    Zhang, H., Zhao, K., Song, Y.Z., Guo, J.: Text extraction from natural scene image: a survey. Neurocomputing 122, 310 (2013)CrossRefGoogle Scholar
  6. 6.
    Gonzalez, A., Bergasa, L.M.: A text reading algorithm for natural images. Image Vis. Comput. 31, 255 (2013)CrossRefGoogle Scholar
  7. 7.
    Sun, L., Huo, Q., Jia, W., Chen, K.: A robust approach for text detection from natural scene images. Pattern Recogn. 48, 2906 (2015)CrossRefGoogle Scholar
  8. 8.
    Liu, J., Su, H., Yi, Y., Hu, W.: Robust text detection via multi-degree of sharpening and blurring. Signal Process. 124, 259 (2015)CrossRefGoogle Scholar
  9. 9.
    Minetto, R., Thome, N., Cord, M., Leite, N.J., Stolfi, J.: SnooperText: a text detection system for automatic indexing of urban scenes. Comput. Vis. Image Underst. 122, 92 (2014)CrossRefGoogle Scholar
  10. 10.
    Azam, S., Islam, M.M.: Automatic license plate detection in hazardous condition. J. Vis. Commun. Image Represent. 36, 172 (2016)CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Ban, X., Chen, J., Hu, B., Yang, X.: License plate recognition based on SIFT feature. Optik Int. J. Light Electron Opt. 126, 2895 (2016)CrossRefGoogle Scholar
  12. 12.
    Neto, E.C., Gomes, S.L., Filho, P.P.R., de Albuquerque, V.H.C.: Brazilian vehicle identification using a new embedded plate recognition system. Measurement 70, 36 (2015)CrossRefGoogle Scholar
  13. 13.
    Tian, J., Wang, R., Wang, G., Liu, J., Xia, Y.: A two-stage character segmentation method for chinese license plate. Comput. Electr. Eng. 46, 539 (2015)CrossRefGoogle Scholar
  14. 14.
    Jawas, N.: Image based automatic water meter reader. J. Phys. Conf. Ser. 953, 012027 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory LAROSERI, Department of Computer Science, Faculty of ScienceChouaib Doukkali UniversityEI JadidaMorocco

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