Abstract
One crucial issue in automatic document analysis is the discrimination between text and graphics/images. This paper presents a novel, robust method for the segmentation of text and graphics/images in digitized documents. This method is based on the representation of window-like portions of a document by means of their gray level histograms. Through empirical evidence it is shown that text and graphics/images regions have different gray level histograms. Unlike the usual approach for the characterization of histograms that is based on statistics parameters a novel approach is introduced. This approach works with the histogram Fourier transform since it possesses all the information contained in the histogram pattern. The next and logical step is to automatically select the most discriminant spectral components as far as the text and graphics/images segmentation goal is concerned. A fully automated procedure for the optimal selection of the discriminant features is also expounded. Finally, empirical results obtained for the text and graphics/images segmentation using a simple three-layer perceptron-like neural network are also discussed.
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References
H. Bunke, P.S.P. Wang, Handbook of Character Recognition and Document Image Analysis, World Scientific (1997).
Z. Jiang, J. Liu, J. Liu Jin“Char: a tool for automatic text/graphics segmentation of engineering drawing images”, Journal of Software Vol. 10 No. 6 (June 1999) 589–594.
Z. Jiang, J. Liu, “A shielding method for segmentation of graphics touching text in engineering drawings”, Porceedings of the SPIE. The International Society for Optical Engineering, Vol. 3305 (1998) 53–60.
K. Tombre, C. Ah Soon, P. Dosch, A. Habed,, G. Masini, “Stable, robust and off-the-shelf methods for graphics recognition”, Proceedings. Fourteenth Conference on Pattern Recognition. IEEE Comput. Soc, Vol. 1 (1998) 406–408.
E. Martí, “Análisis de elementos gráficos en documentos”, RECV-Revista Electrónica de Visión por Computador, Centro de Visión por Computador, No. 0, (Octubre 1999). http://www.cvc.uab.es/recv/revista/000/00001-tut.htm.
T. Ojala, M. Pietikäinen, and J. Nisula, “Determining composition of grain mixtures by texture classification based on feature distributions”, Int. Journal of Pattern Recognition and Artificial Intelligence Vol. 10 No. 1 (1996) 73–82.
T. Ojala, M. Pietikäinen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions” Proc. 12th. Int. Conf. On Pattern Recognition, vol. 1, Jerusalem, Israel, (1994) 582–585.
M. Unser, “Sum and difference histograms for texture classification”, IEEE Trans. on Pattern Anal. and Machine Intell. 8 (1986) 118–125.
A. L. Vickers and J.W. Modestino, “A maximum likelihood aproach to texture classification”, IEEE Trans. on Pattern Anal. and Machine Intell. 4 (1982) 61–68.
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© 2000 Springer-Verlag Berlin Heidelberg
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Patricio, M.A., Maravall, D. (2000). Segmentation of Text and Graphics/Images Using the Gray-Level Histogram Fourier Transform. In: Ferri, F.J., Iñesta, J.M., Amin, A., Pudil, P. (eds) Advances in Pattern Recognition. SSPR /SPR 2000. Lecture Notes in Computer Science, vol 1876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44522-6_78
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DOI: https://doi.org/10.1007/3-540-44522-6_78
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