Abstract
The bounding-box of a geometric shape in 2D is the rectangle with the smallest area in a given orientation (usually upright) that complete contains the shape. The best-fit bounding-box is the smallest bounding-box among all the possible orientations for the same shape. In the context of document image analysis, the shapes can be characters (individual components) or paragraphs (component groups). This paper presents a search algorithm for the best-fit bounding-boxes of the textual component groups, whose shape are customarily rectangular in almost all languages. One of the applications of the best-fit bounding-boxes is the skew estimation from the text blocks in document images. This approach is capable of multi-skew estimation and location, as well as being able to process documents with sparse text regions. The University of Washington English Document Image Database (UW-I) is used to verify the skew estimation method directly and the proposed best-fit bounding-boxes algorithm indirectly.
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References
Nagy, G.: Twenty Years of Document Image Analysis in PAMI. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 38–62 (2000)
O’Gorman, L., Kasturi, R.: Document Image Analysis. IEEE Computer Society Press, Los Alamitos (1995)
Cattoni, R., Coianiz, T., Messelodi, S., Modena, C.M.: Geometric Layout Analysis Techniques for Document Image Understanding: a Review. ITC-IRST Technical Report #9703-09 (1998)
Postl, W.: Detection of Linear Oblique Structures and Skew Scan in Digitized Documents. In: Proceedings of the 8th International Conference on Pattern Recognition, Paris, October 1986, pp. 687–689 (1986)
Baird, H.S.: The Skew Angle of Printed Documents. In: Proceedings of the SPSE 40th Annual Conference and Symposium on Hybrid Imaging Systems, Rochester, NY, May 1987, pp. 21–24 (1987)
Nakano, Y., Shima, Y., Fujisawa, H., Higashino, J., Fujinawa, M.: An algorithm for skew normalization of document images. In: Proceedings of the 10th International Conference on Pattern Recognition, Atlantic City, New Jersey, pp. 8–13 (1990)
Spitz, A.L.: Skew Determination in CCITT Group 4 Compressed Images. In: Proceedings of the 1st Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, March 16-18, pp. 11–25 (1992)
Srihari, S.N., Govindaraju, V.: Analysis of Textual Images Using the Hough Transform. Machine Vision and Applications 2(3), 141–153 (1989)
Hinds, S., Fisher, J., D’Amato, D.: A document skew detection method using run-length encoding and the Hough transform. In: Proceedings of the 10th International Conference on Pattern Recognition, Atlantic City NJ, June 17-21, pp. 464–468 (1990)
Chen, S., Haralick, R.M.: An Automatic Algorithm for Text Skew Estimation in Document Images Using Recursive Morphological Transforms. In: Proceedings of IEEE International Conference on Image Processing, Austin TX, November 13-16, pp. 139–143 (1994)
Aghajan, H.K., Kailath, T.: SLIDE: Subspace-Based Line Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(11), 1057–1073 (1994)
Yuan, B., Tan, C.L.: Fiducial line based skew estimation. Pattern Recognition 38(12), 2333–2350 (2005)
Yuan, B., Tan, C.L.: A Multi-Level Component Grouping Algorithm and Its Applications. In: Proceedings of the 8th International Conference on Document Analysis and Recognition, Seoul Korea, August 29 - September 1, pp. 1178–1181 (2005)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)
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© 2006 Springer-Verlag Berlin Heidelberg
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Yuan, B., Kwoh, L.K., Tan, C.L. (2006). Finding the Best-Fit Bounding-Boxes. In: Bunke, H., Spitz, A.L. (eds) Document Analysis Systems VII. DAS 2006. Lecture Notes in Computer Science, vol 3872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11669487_24
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DOI: https://doi.org/10.1007/11669487_24
Publisher Name: Springer, Berlin, Heidelberg
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