Abstract
Correct detection of line attributes by line detection algorithms is important and leads to good quality vectors. Line attributes includes: end points, width, line style, line shape, and center (for arcs). In this paper we study different factors that affect detected vector attributes. Noise level, cleaning method, and vectorization software are three factors that may influence the resulting vector data attributes. Real scanned images from GREC’03 and GREC’07 contests are used in the experiment. Three different levels of salt-and-pepper noise (5%, 10%, and 15%) are used. Noisy images are cleaned by six cleaning algorithms and then three different commercial raster to vector software are used to vectorize the cleaned images. Vector Recovery Index (VRI) is the performance evaluation criteria used in this study to judge the quality of the resulting vectors compared to their ground truth data. Statistical analysis on the VRI values shows that vectorization software has the biggest influence on the quality of the resulting vectors.
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References
Tombre, K.: Graphics recognition: The last ten years and the next ten years. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 422–426. Springer, Heidelberg (2006)
Liu, W.: Report of the Arc Segmentation Contest. In: Graphics Recognition: Lecture Notes in Computer Science: Recent Advances and Perspectives, pp. 363–366. Springer, Heidelberg (2004)
Wenyin, L.: The third report of the arc segmentation contest. In: Liu, W., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 358–361. Springer, Heidelberg (2006)
Shafait, F., Keysers, D., Breuel, T.M.: GREC 2007 Arc Segmentation Contest: Evaluation of Four Participating Algorithms, vol. 5046. Springer, Heidelberg (2007)
O’Gorman, L.: Image and document processing techniques for the RightPages electronic library system. In: Proc. 11th IAPR International Conference on Pattern Recognition. Conference B: Pattern Recognition Methodology and Systems, The Hague, pp. 260–263 (1992)
Story, G.A., O’Gorman, L., Fox, D., Schaper, L.L., Jagadish, H.V.: The RightPages image-based electronic library for alerting and browsing. Computer 25(9), 17–26 (1992)
Chinnasarn, K., Rangsanseri, Y., Thitimajshima, P.: Removing salt-and-pepper noise in text/graphics images. In: The 1998 IEEE Asia-Pacific Conference on Circuits and Systems, Chiangmai, pp. 459–462 (1998)
Simard, P.Y., Malvar, H.S.: An efficient binary image activity detector based on connected components. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 229–233 (2004)
Al-Khaffaf, H.S.M., Talib, A.Z., Abdul Salam, R.: Internal Report, Artificial Intelligence Research Group, School of Computer Sciences, Universiti Sains Malaysia (2006)
Vectory 5.0. Raster to Vector Conversion Software. Graphikon GmbH, Berlin, Germany, http://www.graphikon.de
VPstudio ver 8.02 C6. Raster to Vector Conversion Software, Softelec, Munich, Germany, http://www.softelec.com , http://www.hybridcad.com
Scan2CAD 7.5d. Raster to Vector Conversion Software, Softcover International Limited, Cambridge, England, http://www.softcover.com
Phillips, I.T., Chhabra, A.K.: Empirical performance evaluation of graphics recognition systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 849–870 (1999)
Chhabra, A.K., Phillips, I.T.: Performance evaluation of line drawing recognition systems. In: Proc. 15th International Conference on Pattern Recognition, Barcelona, pp. 864–869 (2000)
Liu, W.Y., Dori, D.: A protocol for performance evaluation of line detection algorithms. Machine Vision and Applications 9(5-6), 240–250 (1997)
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Al-Khaffaf, H.S.M., Talib, A.Z., Abdul Salam, R. (2008). A Study on the Effects of Noise Level, Cleaning Method, and Vectorization Software on the Quality of Vector Data. In: Liu, W., Lladós, J., Ogier, JM. (eds) Graphics Recognition. Recent Advances and New Opportunities. GREC 2007. Lecture Notes in Computer Science, vol 5046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88188-9_28
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DOI: https://doi.org/10.1007/978-3-540-88188-9_28
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