Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2005.03.023
Title: Fiducial line based skew estimation
Authors: Yuan, B. 
Tan, C.L. 
Keywords: Centroids
Component pairs
Fiducial lines
Noise immunity
Skew estimation
UWDB-I
Issue Date: 2005
Source: Yuan, B., Tan, C.L. (2005). Fiducial line based skew estimation. Pattern Recognition 38 (12) : 2333-2350. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2005.03.023
Abstract: Skew estimation for textual document images is a well-researched topic and numerals of methods have been reported in the literature. One of the major challenges is the presence of interfering non-textual objects of various types and quantities in the document images. Many existing methods require proper separation of the textual objects which are well aligned from the non-textual objects which are mostly nonaligned. Some comparative evaluation work on the existing methods chooses only the text zones of the test image database. Therefore, the object filtering or zoning stage is crucial to the skew detection stage. However, it is difficult if not impossible to design general-purpose filters that are able to discriminate noises from textual components. This paper presents a robust, general-purpose skew estimation method that does not need any filtering or zoning preprocessing. In fact, this method does apply filtering, but not on the input components at the beginning of the detection process, rather on the output spectrum at the end of the detection process. Therefore, the problem of finding a textual component filter has been transformed into finding a convolution filter on the output accumulator array. This method consists of three steps: (1) the calculation of the slopes of the virtual lines that pass through the centroids of all the unique pairs of the connected components in an image, and quantizes the arctangents of the slopes into a 1-D accumulator array that covers the range from -90° to +90°; (2) a special convolution on the resultant histogram, after which there remain only the prominent peaks that possibly correspond to the skew angles of the image; (3) the verification of the detection result. Its computational complexity and detection precision are uncoupled, unlike those projection-profile-based or Hough-transform-based methods whose speeds drop when higher precision is in demand. Speedup measures on the baseline implementation are also presented. The University of Washington English Document Image Database I (UWDB-I) contains a large number of scanned document images with significant amount of non-textual objects. Therefore, it is a good image database for evaluating the proposed method. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/39856
ISSN: 00313203
DOI: 10.1016/j.patcog.2005.03.023
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