• DocumentCode
    2999421
  • Title

    Script independent angular skew detection and correction algorithms

  • Author

    Mathur, Utkarsh ; Sharma, Ritu ; Srivastava, N.

  • Author_Institution
    Dept. of Electron. & Commun. Eng., Jaypee Inst. of Inf. Technol., Noida, India
  • fYear
    2013
  • fDate
    12-14 Dec. 2013
  • Firstpage
    466
  • Lastpage
    469
  • Abstract
    Document digitalization is done in a large scale for hand written and printed documents, where the documents are scanned and stored in a digital form. Maximum number of these documents are hand written, and hence, they comprise of various errors like angular skewness of words, or sentence as a whole. Before the document can be digitalised using OCR software, it is pre-processed for angular skew detection and their removal from the scanned document. Detection and correction of angular skewness of the words is the most difficult task for the process, which must be carried out as this skewness is present in almost all of the hand written words. In this paper, we propose and test algorithms independent of the script to detect and correct angular skewness.
  • Keywords
    document image processing; handwritten character recognition; natural language processing; object detection; optical character recognition; OCR software; angular word skewness; digital form; document digitalization; hand written documents; optical character recognition; printed documents; script independent angular skew correction algorithm; script independent angular skew detection algorithm; Accuracy; Character recognition; Histograms; Optical character recognition software; Software algorithms; Transforms; Document pre-processing; Skew detection; angular skew; skew correction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communication (ICSC), 2013 International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-1605-4
  • Type

    conf

  • DOI
    10.1109/ICSPCom.2013.6719835
  • Filename
    6719835