IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v21y2019i1p63-75.html
   My bibliography  Save this article

A novel boot strapping algorithm for text extraction in a self-organising neural network model

Author

Listed:
  • Xiaohong Li
  • Maolin Li

Abstract

With rapid growth in internet and its associated communication protocols, need for printed documents to be carried over from one place to another has been reduced to minimise the cost and time. Research contributions in the past have paved the way for implementation of smart and intelligent algorithms to further minimise manual intervention in processing of documents. One such area is the automation of text extraction from documents with increased accuracy and least number of false detections. A wide range of algorithms and methodologies have been reported in the past towards efficient extraction of text from documents which may be online or offline. This research paper proposes an efficient extraction algorithm of text from given set of documents which may or may not be graphic through utilisation of a hybrid SOM-ANN algorithm. The experimentation has been done over a wide variety of inputs and convergence of error in extraction is found to be minimum when compared to other conventional extractors.

Suggested Citation

  • Xiaohong Li & Maolin Li, 2019. "A novel boot strapping algorithm for text extraction in a self-organising neural network model," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 21(1), pages 63-75.
    • Handle: RePEc:ids:ijnvor:v:21:y:2019:i:1:p:63-75
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=101148
    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:ijnvor:v:21:y:2019:i:1:p:63-75. 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=22 .

    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.