Skew Detection Techniques used in Degraded Document Images

Authors(2) :-Snehal S. Kolhe, K. T. Jadhao

In this paper skew detection technique for printed and handwritten devnagri scanned documents is proposed. Recognition methods for printed and handwritten texts in Scanned documents are significantly different. Skew detection technique consists of processing steps like preprocessing, segmentation, skew correction and detection with enhancement which is used to identify writer. Segmentation is used to extract text lines and words from handwritten and printed documents. A horizontal projection and vertical projection algorithm is used to segment the document into a number of lines. Nilback, Sauvola’s and wolfs algorithm is used for binarization. Linear Otsu thresholding is used for filter. The scan line method is used for the skew detection and correction .Adaptive histogram algorithm is used for image enhancement. Various Printed and handwritten text documents are scanned for the different preprocessing methods. In segmentation, for the Line, word, character segmentation we used the separate algorithms. For the skew detection and correction, skew line method gives the better accuracy.

Authors and Affiliations

Snehal S. Kolhe
EXTC Department, Mumbai University, Maharastra, India
K. T. Jadhao
EXTC Department, Mumbai University, Maharastra, India

Pre-Processing, Filtering, Segmentation, Skew Detection and Correction, Enhancement

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 414-420
Manuscript Number : IJSRSET162455
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Snehal S. Kolhe, K. T. Jadhao, " Skew Detection Techniques used in Degraded Document Images, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 4, pp.414-420, July-August-2016.
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