AUTOMATIC DOCUMENT SKEW PRE-PROCESSOR FOR CHARACTER SEGMENTATION ALGORITHM

Vladan Vučković, Boban Arizanovic

DOI Number
10.2298/FUEE1704611V
First page
611
Last page
625

Abstract


In this paper, as a pre-processing part of character segmentation system, an automatic document skew correction approach, based on Hough transform, is presented. Document skew is a common pre-processing problem and diverse approaches have been proposed in the past. The importance of solving this problem lies in the fact that further character segmentation process is impossible if document image is skewed. The character segmentation algorithm extended with skew correction algorithm has been proved to be efficient since the experimental results showed that successful character segmentation percentage is higher than 85 after the skew correction. Character segmentation algorithm results for skewed and de-skewed documents are also provided using the original Nikola Tesla’s documents from the “Nikola Tesla Museum”.

Keywords

Skew correction, Hough transform, character segmentation, spatial transformations, rotation, fast algorithm

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References


S. S. Cvetković, S. V. Nikolić and S. Ilić, “Effective combining of color and texture descriptors for indoor-outdoor image classification”, Facta Universitatis: Electronics and Energetics, vol. 27, no. 3, pp. 399-410, 2014.

J. J. Hull, “Document image skew detection: Survey and annotated bibliography”, Series in Machine Perception and Artificial Intelligence, vol. 29, pp. 40-66, 1998.

H. S. Baird, “The skew angle of printed documents”, Document image analysis, pp. 204-208, 1995.

A. Papandreou et al., “ICDAR2013 Document Image Skew Estimation Contest (DISEC’13)”, In Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), 2013.

A. D. Bagdanov and J. Kanai, “Evaluation of document image skew estimation techniques”, In SPIE Proceedings 2660: Document Recognition III, 1996, pp. 343-354.

P. Mukhopadhyay and B. B. Chaudhuri, “A survey of Hough Transform”, Pattern Recognition, vol. 48, no. 3, pp. 993-1010, 2015.

R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures”, In proceedings of the Communications of the ACM, vol. 15, no. 1, pp. 11-15, 1972.

S. N. Srihari and V. Govindaraju, “Analysis of textual images using the Hough transform”, Machine Vision and Applications, vol. 2, no. 3, pp. 141-153, 1989.

O. G. Okun, “Geometrical approach to skew detection for documents containing the Latin/Cyrillic characters”, In Proceedings of the SPIE, vol. 3811: Vision Geometry VIII, 1999, pp. 357-365.

A. Boukharouba, “A new algorithm for skew correction and baseline detection based on the randomized Hough Transform”, Journal of King Saud University - Computer and Information Sciences, vol. 29, no. 1, pp. 29-38, 2016.

D. Kumar and D. Singh, “Modified approach of Hough transform for skew detection and correction in documented images”, International Journal of Research in Computer Science, vol. 2, no. 3, pp. 37-40, 2012.

F. Stahlberg and S. Vogel, “Document Skew Detection Based on Hough Space Derivatives”, In Proceedings of the 13th International Conference on Document Analysis and Recognition, 2015.

V. Shapiro, “Accuracy of the straight line Hough Transform: The non-voting approach”, Computer Vision and Image Understanding, vol. 103, no. 1, pp. 1-21, 2006.

S. Guo et al., “An improved Hough transform voting scheme utilizing surround suppression”, Pattern Recognition Letters, vol. 30, no. 13, pp. 1241-1252, 2009.

B. Gatos, N. Papamarkos and C. Chamzas, “Skew detection and text line position determination in digitized documents”, Pattern Recognition, vol. 30, no. 9, pp. 1505-1519, 1997.

U. Pal and B. B. Chaudhuri, “An improved document skew angle estimation technique”, Pattern Recognition Letters, vol. 17, no. 8, pp. 899-904, 1996.

A. Amin et al., “Fast algorithm for skew detection”, In Proceedings of the SPIE 2661: Real-Time Imaging, 1996, pp. 65-77.

C. Singh, N. Bhatia and A. Kaur, “Hough transform based fast skew detection and accurate skew correction methods”, Pattern Recognition, vol. 41, no. 12, pp. 3528-3546, 2008.

L. A. F. Fernandes and M. M. Oliveira, “Real-time line detection through an improved Hough transform voting scheme”, Pattern Recognition, vol. 41, no. 1, pp. 299-314, 2008.

L. A. Najman, “Using mathematical morphology for document skew estimation”, In Proceedings of the SPIE 5296: Document Recognition and Retrieval XI, 2003, pp. 182-192.

G. Bessho, K. Ejiriand J. F. Cullen, “Fast and accurate skew detection algorithm for a text document or a document with straight lines”, In Proceedings of the SPIE 2181: Document Recognition, 1994, pp. 133-141.

J. van Beusekomand T. M. Breuel, “Resolution independent skew and orientation detection for document images”, In Proceedings of the SPIE 7247: Document Recognition and Retrieval XVI, 2009, pp. 72470K-72470K-8.

R. Kapoor, D. Bagai and T. S. Kamal, “A new algorithm for skew detection and correction”, Pattern Recognition Letters, vol. 25, no. 11, pp. 1215-1229, 2004.

H. Liu et al., “Skew detection for complex document images using robust borderlines in both text and non-text regions”, Pattern Recognition Letters, vol. 29, no. 13, pp. 1893-1900, 2008.

Y. Cao, S. Wang and H. Li, “Skew detection and correction in document images based on straight-line fitting”, Pattern Recognition Letters, vol. 24, no. 12, pp. 1871-1879, 2003.

J. Fabrizio, “A precise skew estimation algorithm for document imagesusing KNN clustering and Fourier transform,” In Proceedings of the IEEE International Conference on Image Processing (ICIP), 2014.

A. Chan-Hon-Tong, C. Achard and L. Lucat, “Simultaneous segmentation and classification of human actions in video streams using deeply optimized Hough transform”, Pattern Recognition, vol. 47, no. 12, pp. 3807-3818, 2014.

C. Tu et al., “Vehicle Position Monitoring Using Hough Transform”, In Proceedings of the International Conference on Electronic Engineering and Computer Science, vol. 4, pp. 316-322.

R. Varun et al., “Face Recognition Using Hough Transform Based Feature Extraction”, Procedia Computer Science, vol. 46, pp. 1491-1500, 2015.

V. Vučković and S. Spasić, “3-D stereoscopic modeling of the Tesla’s Long Island”, Facta Universitatis: Electronics and Energetics, vol. 29, no. 1, pp. 113-126, 2016.


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