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Speed-up ellipse enclosing character detection approach for large-size document images by parallel scanning and Hough transform

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Abstract

This paper presents a speed-up ellipse enclosing character detection algorithm that uses parallel image scanning and the Hough transform (HT) for large-size document images. Objects in images are generally detected based on geometrical information obtained via raster scanning. In raster scanning, all pixels of an image are scanned starting from the upper-left point and ending with the lower-right point. In the case of large-size images, considerable time is needed for processing an image by scanning all pixels. In this paper, an object detection approach for large-size images is proposed which does not require scanning all pixels in the images. In this speed-up ellipse enclosing character detection approach for large-size document images, pixels are scanned on constantly spaced vertical parallel lines. If an object larger than a certain size is identified while scanning, the presence of an ellipse enclosing character is assumed and ellipse detection is conducted by applying HT only in a defined local image area over the found object. With this approach, processing time can be dramatically reduced by disregarding some objects and reducing the total image area used for ellipse detection.

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References

  1. Jin S, You Y, Huafen Y (2010) Scanned Document Image Processing Model for Information System. In: Proc. of Asia-Pacific Conference onWearable Computing Systems (APWCS). pp 198–201

  2. Wang Q, Chi Z, Zhao R (2002) Hierarchical content classification and script determination for automatic document image processing. In: Proc. of 16th International Conference on Pattern Recognition. pp 77–80

  3. Yip SK, Chi Z (2001) Page segmentation and content classification for automatic document image processing. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. pp 279–282

  4. Manikandan V, Venkatachalam V, Kirthiga M, Harini K, Devarajan N (2010) An enhanced algorithm for Character Segmentation in document image processing. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). pp 1–5

  5. Yang Y, Yan H A robust document processing system combining image segmentation with content-based document compression. In: Proceedings. 15th International Conference on Pattern Recognition. pp 519–522

  6. Borges PVK, Mayer J, Izquierdo E (2008) Document image processing for paper side communications. IEEE Trans Multimedia 10(7):1277

    Article  Google Scholar 

  7. Shi Z, Setlur S, Govindaraju V (2013) A model based framework for table processing in degraded document images. In: 12th International Conference on Document Analysis and Recognition (ICDAR). pp 963–967

  8. Takasu A, Satoh S, Katsura E (1995) A rule learning method for academic document image processing. In: Proceedings of the Third International Conference on Document Analysis and Recognition. pp 239–242

  9. Cao R, Tan CL, Shen P (2001) A wavelet approach to double-sided document image pair processing. Proceedings 2001 International Conference on Image Processing. pp 174–177

  10. Parodi P, Piccioli G (1996) An efficient pre-processing of mixed-content document images for OCR systems. Proceedings of the 13th International Conference on Pattern Recognition. pp 778–782

  11. Baier PE (1995) Image processing of forensic documents. In: Proceedings of the Third International Conference on Document Analysis and Recognition. pp 1–4

  12. Le DX, Thoma GR, Wechsler H (1995) Document image analysis using integrated image and neural processing. In: Proceedings of the Third International Conference on Document Analysis and Recognition. pp 327–330

  13. Li Y, Lalonde M, Reiher E, Rizand JF, Zhu CJ (1997) A knowledge-based image understanding environment for document processing. Proceedings of the Fourth International Conference on Document Analysis and Recognition. pp 979–983

  14. Rosner D, Boiangiu CA, Stefanescu A, Tapus N, Olteanu A (2010) Text line processing for high-confidence skew detection in image documents. In: IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). pp 129–132

  15. Kawanaka H, Sumida T, Yamamoto K, Shinogi T, Tsuruoka S (2007) Document recognition and XML generation of tabular form discharge summaries for analogous case search system. Method Inf Med 46:700–708

    Google Scholar 

  16. Tsuruoka S, Hirano C, Yoshikawa T, Shinogi T (2001) Image-based structure analysis for a table of contents and conversion to XML documents. In: Proceedings of Document Layout Interpretation and its Application. pp 59–62

  17. Premachandra HWH, Premachandra C, Parape DC (2013) Parallel scanning based speed-up method for detection of elliptical obstacles in high-resolution image. Int J Comput Sci Commun Netw 3(4):265–270

    Google Scholar 

  18. Tsuji S, Matsumoto F (1978) Detection of ellipses by a modified Hough transform. IEEE Trans Comput 27(8):777–781

    Article  Google Scholar 

  19. Davis ER (1989) Finding ellipses using generalized Hough transform. Pattern Recog Lett 9:87–96

    Article  Google Scholar 

  20. Yip RKK, Tam PKS, Leung DNK (1992) Modification of Hough transform for circles and ellipses detection using a 2-dimensional array. Pattern Recogn 25(9):1007–1022

    Article  Google Scholar 

  21. Ballard DH (1981) Generalized Hough transform to detect arbitrary patterns. IEEE Trans Pattern Anal Mach Intell 13(2):111–122

    MATH  Google Scholar 

  22. Xie Y, Ji Q (2002) A new efficient ellipse detection method. In: Proceedings of International conference on Pattern Recognition. pp 957–960

  23. Nair PS, Saunders AT (1996) Hough transform based ellipse detection algorithm. Pattern Recognit Lett 17:777–784

    Article  Google Scholar 

  24. Gu Y, Yendo T, Tehrani MP, Fujii T, Tanimoto M (2011) Traffic sign recognition using hybrid camera system. J Inst Image Inf Telev Eng 65(7):967–975

    Google Scholar 

  25. Ho C, Chen L (1995) A fast ellipse/circle detector using geometry. In: Pattern Recognition. pp 117–124

  26. Aguado AS, Montiel ME, Nixon MS (1996) On using directional information for parameter space decomposition in ellipse detection. Pattern Recogn 29:369–381

    Article  Google Scholar 

  27. Ji Q, Haralick RM (1999) A statistically efficient method for ellipse detection. In: Proc. of International Conference on Image Processing. pp 730–734

  28. Ito Y, Ogawa K, Nakano K (2011) Fast ellipse detection algorithm using Hough transform on the GPU. In: Proc. of Second International Conference on Networking and Computing. pp 313–319

  29. Song G, Wang H (2007) A fast and robust ellipse detection algorithm based on pseudo-random sample consensus. Lect Notes Comput Sci 4673:669–676

    Article  Google Scholar 

  30. Otsu N (1978) Discriminant and latest squares threshold selecton. In: Proc of 4IJPCPR 1978. pp 592–596

  31. Otsu N (1979) Threshold detection method from Grey-Level Histograms. In: IEEE Trans. Systems, Man, and Cybernities SMC-9 (No.1). pp 62–66

  32. Okuyama S, Tsuruoka S, Takase H, Kawanaka H, Premachandra C (2014) Interactive learning support user interface for lecture scenes indexed with extracted keyword from black board. Aust J Basic Appl Sci 8(4):319–324

    Google Scholar 

  33. De A, Guo C (2014) An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int J Mach Learn Cybernet 5(4):543–551

    Article  Google Scholar 

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Authors and Affiliations

  1. Wayamba University of Srilanka, Makadura, Sri Lanka

    H. Waruna H. Premachandra

  2. Tokyo University of Science, 6-3-1 Niijuku, Katsushikaku, Tokyo, 125-8585, Japan

    Chinthaka Premachandra

  3. Kyoto University, C1-1-209 Kyoto University Katsura, Nishikyo-Ku, Kyoto, 615-8540, Japan

    Chandana Dinesh Parape

  4. Mie University, 577 Kurimamachiya, Tsu, Mie, 514-8507, Japan

    Hiroharu Kawanaka

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  1. H. Waruna H. Premachandra
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  2. Chinthaka Premachandra
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  3. Chandana Dinesh Parape
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  4. Hiroharu Kawanaka
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Correspondence to Chinthaka Premachandra.

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Premachandra, H.W.H., Premachandra, C., Parape, C.D. et al. Speed-up ellipse enclosing character detection approach for large-size document images by parallel scanning and Hough transform. Int. J. Mach. Learn. & Cyber. 8, 371–378 (2017). https://doi.org/10.1007/s13042-015-0330-0

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