IDEAS home Printed from https://ideas.repec.org/a/igg/jirr00/v9y2019i1p65-87.html
   My bibliography  Save this article

Real Time License Plate Recognition from Video Streams using Deep Learning

Author

Listed:
  • Saquib Nadeem Hashmi

    (Jaypee Institute of Information Technology, Noida, India)

  • Kaushtubh Kumar

    (Jaypee Institute of Information Technology, Noida, India)

  • Siddhant Khandelwal

    (Jaypee Institute of Information Technology, Noida, India)

  • Dravit Lochan

    (Jaypee Institute of Information Technology, Noida, India)

  • Sangeeta Mittal

    (Jaypee Institute of Information Technology, Noida, India)

Abstract

With ever increasing number of vehicles, vehicular management is one of the major challenges faced by urban areas. Automation in terms of detecting vehicle license plate using real time automatic license plate recognition (RT-ALPR) approach can have many use cases in automated defaulter detection, car parking and toll management. It is a computationally complex task that has been addressed in this work using a deep learning approach. As compared to previous approaches, license plates have been recognized from full camera stills as well as parking videos with noise. On a dataset of 4800 car images, the accuracy obtained is 91% on number plate extraction from images, 93% on character recognition. Proposed ALPR system has also been applied to vehicle videos shot at parking exits. Overall 85% accuracy was obtained in real-time license number recognition from these videos.

Suggested Citation

  • Saquib Nadeem Hashmi & Kaushtubh Kumar & Siddhant Khandelwal & Dravit Lochan & Sangeeta Mittal, 2019. "Real Time License Plate Recognition from Video Streams using Deep Learning," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 9(1), pages 65-87, January.
    • Handle: RePEc:igg:jirr00:v:9:y:2019:i:1:p:65-87
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2019010105
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jirr00:v:9:y:2019:i:1:p:65-87. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.