IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/vyid10.1007_s10796-016-9699-x.html
   My bibliography  Save this article

An enhanced text detection technique for the visually impaired to read text

Author

Listed:
  • S. P. Faustina Joan

    (Anna University)

  • S. Valli

    (Anna University)

Abstract

An enhanced text detection technique (ETDT) is proposed, which is expected to aid the visually impaired to overcome their reading challenges. This work enhances the edge-preserving maximally stable extremal regions (eMSER) algorithm using the pyramid histogram of oriented gradients (PHOG). Histogram of oriented gradients (HOG) derived from different pyramid levels is important while detecting maximally stable extremal regions (MSER) in the ETDT approach because it gives more spatial information when compared to HOG information from a single level. To group text, a four-line, text-grouping method is newly designed for this work. Also, a new text feature, Shapeness Score is proposed, which significantly identifies text regions when combined with the other features based on morphology and stroke widths. Using the feature vector of dimension 10, the J48 decision tree and AdaBoost machine learning algorithms identify the text regions in the images. The algorithm yields better results than the existing benchmark algorithms for the ICDAR 2011 born-digital dataset and must be improved with respect to the scene text dataset.

Suggested Citation

  • S. P. Faustina Joan & S. Valli, 0. "An enhanced text detection technique for the visually impaired to read text," Information Systems Frontiers, Springer, vol. 0, pages 1-18.
    • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9699-x
    DOI: 10.1007/s10796-016-9699-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-016-9699-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-016-9699-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    References listed on IDEAS

    as
    1. A. Annis Fathima & V. Vaidehi & K. Selvaraj, 2014. "Fall Detection with Part-Based Approach for Indoor Environment," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 10(4), pages 51-69, October.
    2. C. Sweetlin Hemalatha & V. Vaidehi & K. Nithya & A. Annis Fathima & M. Visalakshi & M. Saranya, 2015. "Multi-Level Search Space Reduction Framework for Face Image Database," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 11(1), pages 12-29, January.
    3. S. Shanthi & V. Murali Bhaskaran, 2013. "A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 9(1), pages 21-39, January.
    4. C. Sweetlin Hemalatha & V. Vaidehi, 2013. "Associative Classification based Human Activity Recognition and Fall Detection using Accelerometer," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 9(3), pages 20-37, July.
    5. Kenneth McLeod & D. N. F. Awang Iskandar & Albert Burger, 2013. "Towards the Semantic Representation of Biological Images: From Pixels to Regions," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 9(4), pages 35-54, October.
    6. Xiafen Zhang & Vijayan Sugumaran, 2014. "Content Based Search Engine for Historical Calligraphy Images," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 10(3), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mengyue Wang & Xin Li & Patrick Y. K. Chau, 2021. "Leveraging Image-Processing Techniques for Empirical Research: Feasibility and Reliability in Online Shopping Context," Information Systems Frontiers, Springer, vol. 23(3), pages 607-626, June.
    2. Vijayan Sugumaran & T. V. Geetha & D. Manjula & Hema Gopal, 2017. "Guest Editorial: Computational Intelligence and Applications," Information Systems Frontiers, Springer, vol. 19(5), pages 969-974, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. P. Faustina Joan & S. Valli, 2017. "An enhanced text detection technique for the visually impaired to read text," Information Systems Frontiers, Springer, vol. 19(5), pages 1039-1056, October.

    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:spr:infosf:v::y::i::d:10.1007_s10796-016-9699-x. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.