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A Graph-based Method to Remove Interferential Curve From Text Image

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Abstract

A general algorithm for eliminating interferential curve in text image was proposed, which converted images into basic graph and super graph first, and then the interferential curve was treated as principal curve to be detected. In the detection, improved DFS algorithm, shortest path algorithm and orientation offset algorithm were used. Finally, the detected curve was removed from the original image, leaving only text in the image. Experiments conducted with a variety of text images showed that this algorithm is effective in removing interferential curve from text image.

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References

  1. Ahmed M.E., Mohamed A.I.(2001). A graph-based segmentation and feature extraction framework for Arabic text recognition. In: Proceedings, Sixth International Conference on Document Analysis and Recognition, pp. 622–626

  2. Amano, A., Asada, N.: Graph grammar based analysis system of complex table form document. In: Proceedings, Seventh International Conference on Document Analysis and Recognition, pp. 916–920 (2003)

  3. Bai, Z.L., Huo, Q.: A goal-oriented verification-based approach for target text line extraction from a document image captured by a pen scanner. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2,23–26 August, pp. 574–577 (2004)

  4. Chen C.S., Tsai W.H.(1990). A new fast one-pass thinning algorithm and its parallel hardware implementation. Pattern Recognit. Lett. 11(7): 471–477

    Article  MATH  Google Scholar 

  5. Dori D., Velkovitch Y.(1998). Segmentation and recognition of dimensioning text from engineering drawings. Comput. Vis. Image Understand. 69(2): 196–201

    Article  Google Scholar 

  6. Habacha, A.: Structural recognition of disturbed symbols using discrete relaxation. In: 1st ICDAR, Saint Malo, France, September–October 1991 pp. 170–78 (1991)

  7. Hiroki, T., Masayuki, N.: Region graph based text extraction from outdoor images. In: Proceedings, Third International Conference on Information Technology and Applications, vol. 1, pp. 680–685(2005)

  8. Llados, J., Lopez-Krahe, J., Marti, E.: Hand drawn document understanding using the straight line Hough transform and graph matching. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 2,pp. 497–501 (1996)

  9. McHugh J.A.(1990). Algorithmic graph theory. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  10. Montes, M., Gelbukh, A., Lopez-Lopez, A., Baeza, R.: Text mining with conceptual graphs. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 2,pp. 898–903 (2001)

  11. Reiher, E., Li, Y., Donne, V.D., Lalonde, M., Hayne, C., Zhu, C.: A system for efficient and robust map symbol recognition. In: Proceedings International Conference Pattern Recognition, vol. 3, August, pp. 783–787 (1996)

  12. Toru W., Yoshimasa K., Akira K.(2001). Affine-invariant recognition of gray-scale characters using global affine transformation correlation. IEEE Trans. Pattern Anal. Mach. Intell. 23(4): 384–395

    Article  Google Scholar 

  13. Trier O.D., Taxt T., Jain A.K.(1997). Recognition of digits in hydrographic maps: binary versus topographic analysis. IEEE Trans. Pattern Anal. Mach. Intell. 19(4): 399–404

    Article  Google Scholar 

  14. Whichello A.P., Yan H.(1996). Fast location of address blocks and postcodes in mail-piece images. Pattern Recognit. Lett. 17(11): 1199–1214

    Article  Google Scholar 

  15. Wong E.K., Chen M.(2003). A new robust algorithm for video text extraction. Pattern Recognit 36(6): 1397–1406

    Article  MATH  Google Scholar 

  16. Xu Y., Nagy G.(1999). Prototype extraction and adaptive OCR. IEEE Trans. Pattern Anal. Mach. Intell. 21(12): 1280–1296

    Article  Google Scholar 

  17. Ye X.Y., Cheriet M., Suen C.Y.(1998). Stroke-model-based character extraction from gray-level document images. IEEE Trans. Image Process 10(8): 1152–1161

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 20030, China

    Zhiguo Cheng & Yuncai Liu

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  1. Zhiguo Cheng
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  2. Yuncai Liu
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Correspondence to Zhiguo Cheng.

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Cheng, Z., Liu, Y. A Graph-based Method to Remove Interferential Curve From Text Image. Machine Vision and Applications 17, 219–228 (2006). https://doi.org/10.1007/s00138-006-0033-3

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  • DOI: https://doi.org/10.1007/s00138-006-0033-3

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