IDEAS home Printed from https://ideas.repec.org/a/ids/ijient/v7y2020i1-2-3p67-80.html
   My bibliography  Save this article

Real time noisy dataset implementation of optical character identification using CNN

Author

Listed:
  • R. Anand
  • T. Shanthi
  • R.S. Sabeenian
  • S. Veni

Abstract

Optical character recognition (OCR) is one of the major research problem in real time applications and it is used to recognise all the characters in an image. As English is a universal language, character recognition in English is a challenging task. Deep learning approach is one of the solution for the recognition of optical characters. Aim of this research work is to perform character recognition using convolutional neural network with LeNET architecture. Dataset used in this work is scanned passport dataset for generating all the characters and digits using tesseract. The dataset has training set of 60,795 and testing set of 7,767. Total samples used are 68,562 which is separated by 62 labels. Till now there is no research on predicting all 52 characters and ten digits. The algorithm used in this work is based on deep learning with appropriate some layer which shows significant improvement in accuracy and reduced the error rate. The developed model was experimented with test dataset for prediction and can produce 93.4% accuracy on training, and 86.5% accuracy on the test dataset.

Suggested Citation

  • R. Anand & T. Shanthi & R.S. Sabeenian & S. Veni, 2020. "Real time noisy dataset implementation of optical character identification using CNN," International Journal of Intelligent Enterprise, Inderscience Enterprises Ltd, vol. 7(1/2/3), pages 67-80.
    • Handle: RePEc:ids:ijient:v:7:y:2020:i:1/2/3:p:67-80
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=104646
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijient:v:7:y:2020:i:1/2/3:p:67-80. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=167 .

    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.