@inproceedings{scholars4271,
            year = {2014},
         journal = {2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings},
            note = {cited By 11; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Document image skew detection and correction method based on extreme points},
             doi = {10.1109/ICCOINS.2014.6868412},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938770494&doi=10.1109\%2fICCOINS.2014.6868412&partnerID=40&md5=8b54ef77fa2252c8eb1d3d8f443014c9},
            isbn = {9781479943913},
        abstract = {In this paper we present a method for estimating the document image skew angle. The main idea of this method is based on the concept that any document image has objects with rectangular shape such as paragraphs, text lines, tables and figuResearch These objects can be bounded by rectangles. We use the extreme point's properties to obtain the corners of the rectangle which fits the largest connected component of the document image. The angle of this rectangle represents the angle of document skew. The experimental results show the high performance of the algorithm in detecting the angle of skew for a variety of documents with different levels of complexity. {\^A}{\copyright} 2014 IEEE.},
          author = {Wagdy, M. and Faye, I. and Rohaya, D.},
        keywords = {Artificial intelligence; Computer science; Computers; Engineering; Industrial engineering; Software engineering, Correction method; Document image analysis; Document images; Extreme points; Largest connected component; Morphology operations; Rectangular shapes; Skew angle estimations, Geometry}
}