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A Mobile Recognition System for Analog Energy Meter Scanning

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

The work presents a mobile platform based system, scanning electricity, gas and water meters. The motivation is the automation of the manual procedure, increasing the reading accuracy and decreasing the human effort. The methodology comprises two stages - digits detection and Optical Character Recognition. The detection of digits is accomplished by a pipeline of operations. Optical Character Recognition is achieved, employing two different approaches - Tesseract OCR and Convolutional Neural Network. The performance evaluation on a vast number of images reports high precision for the algorithms of both stages. Furthermore, Convolutional Neural Network significantly outperforms the Tesseract OCR for all types of meters. The objective of functionality by the limited speed and data storage of mobile devices is also successfully met.

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Author information

Authors and Affiliations

  1. Anyline GmbH, Vienna, Austria

    Martin Cerman & Daniel Albertini

  2. SAIL LABS Technology GmbH, Vienna, Austria

    Gayane Shalunts

Authors
  1. Martin Cerman
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  2. Gayane Shalunts
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  3. Daniel Albertini
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Corresponding author

Correspondence to Martin Cerman .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Cerman, M., Shalunts, G., Albertini, D. (2016). A Mobile Recognition System for Analog Energy Meter Scanning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_23

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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