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An old greek handwritten OCR system based on an efficient segmentation-free approach

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Abstract

Recognition of Old Greek Early Christian manuscripts is essential for efficient content exploitation of the valuable Old Greek Early Christian historical collections. In this paper, we focus on the problem of recognizing Old Greek manuscripts and propose a novel recognition technique that has been tested in a large number of important historical manuscript collections which are written in lowercase letters and originate from St. Catherine’s Mount Sinai Monastery. Based on an open and closed cavity character representation, we propose a novel, segmentation-free, fast and efficient technique for the detection and recognition of characters and character ligatures. First, we detect open and closed cavities that exist in the skeletonized character body. Then, the classification of a specific character or character ligature is based on the protrusible segments that appear in the topological description of the character skeletons. Experimental results prove the efficiency of the proposed approach.

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References

  1. Amin, A., Masini, G.: Machine recognition of cursive Arabic words, application of digital image processing IV. San Diego, CA, vol. SPIE-359, pp. 286–292 (1982)

  2. Brakensiek, A., Rottland, J., Rigoll, G.: Confidence measures for an address reading system. In: 7th International Conference on Document Analysis and Recognition, ICDAR 2003, pp. 294–298 (2003)

  3. Chi Z., Suters M., Yan H. (1995): Separation of single-and double-touching handwritten numeral strings. Opt. Eng. 34, 1159–1165

    Article  Google Scholar 

  4. Chen, C.H., Curtins, J.: Word recognition in a segmentation-free approach to OCR. In: 2nd International Conference on Document Analysis and Recognition (ICDAR’93), pp. 573–576 (2003)

  5. Chen, C.H., Curtins, J.: A Segmentation-free approach to OCR. IEEE Workshop on Applications of Computer Vision, pp. 190–196 (1992)

  6. Duda R., Hart E. (1973): Pattern Classification and Scene Analysis. Wiley, New York

    MATH  Google Scholar 

  7. Eastwood, B., Jennings, A., Harvey, A.: A feature based neural network segmenter for handwritten words. In: International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’97), pp. 286–290. Australia (1997)

  8. Farag R. (1979): Word-level recognition of cursive script. IEEE Trans. Comput. C-28: 172–175

    Google Scholar 

  9. Gatos B., Pratikakis I., Perantonis S.J. (2006): Adaptive degraded document image binarization. Pattern Recogn. 39, 317–327

    Article  MATH  Google Scholar 

  10. Gatos, B., Konidaris, T., Ntzios, K., Pratikakis, I., Perantonis, S.: A segmentation-free approach for keyword search in historical typewritten documents. In: 8th International Conference on Document Analysis and Recognition (ICDAR’05), Seoul, Korea, (2005)

  11. Gorski, N., Anisimov, V., Augustin, E., Baret, O., Price, D., Simon, JC.: A2iA check reader: a family of bank check recognition systems. In: Proccedings of 5th International Conference on Document Analysis and Recognition, pp. 523–526 (1999)

  12. Gonzalez R.C, Woods R.E. (2003): Digital Image Processing. Addison-Wesley, Reading

    Google Scholar 

  13. Guillevic, D., Suen, CY.: HMM word recognition engine. In: 4th International Conference on Document Analysis and Recognition ICDAR97, pp. 544 (1997)

  14. Hirano, T., Okada, Y., Yoda, F.: Field extraction method from existing forms transmitted by facsimile. In: 6th International Conference on Document Analysis and Recognition, ICDAR2001, pp. 738–742 (2001)

  15. Jung D.M., Krishnamoorty M.S., Nagy G., Shapira A. (1996): N-tuple features for OCR revisited. IEEE Trans. PAMI 18(7): 734–745

    Google Scholar 

  16. Kavallieratou, E., Fakotakis, N., Kokkinakis, G.: Handwritten character recognition based on structural characteristics. In: 16th International Conference on Pattern Recognition, pp. 139–142 (2002)

  17. Kim, I.K., Park, R.H.: Local adaptive thresholding based on a water flow model. In: 2nd Japan–Korea Joint Workhop on Computer Vision, pp. 21–27. Japan (1996)

  18. Lee HJ., Chen B. (1992): Recognition of handwritten Chinese characters via short line segments. Pattern Recogn. 25(5): 543–552

    Article  Google Scholar 

  19. Lu Y., Tan C.L. (2002): Combination of multiple classifiers using probabilistic dictionary and its application to postcode recognition. Pattern Recogn. 35, 2823–2832

    Article  MATH  Google Scholar 

  20. Lu Y., Shridhar M. (1996): Character segmentation in handwritten words-an overview. Pattern Recogn. 29(1): 77–96

    Article  Google Scholar 

  21. Madhvanath S., Kleinger E., Govindaraju V. (1999): Holistic verifications of handwritten phrases. IEEE Trans. PAMI 21: 1344–1356

    Google Scholar 

  22. Madhvanath, S., Govindaraju, V.: Holistic lexicon reduction. In: Proceedings of the 3rd International Workshop on Frontiers in Handwriting Recognition, pp.71–82 Buffalo, NY (1993)

  23. Manmatha, R., Croft, WB.: A draft of word spotting: indexing handwritten manuscripts. In: Intelligent Multimedia Information Retrieval, pp. 43–64. MIT Press, Cambridge, MA (1997)

  24. Mori S., Suen CY., Yamamoto K. (1992): Historical review of OCR research and development. Proc. IEEE, 80, 1029–1058

    Article  Google Scholar 

  25. Niblack, W.: An Introduction to Digital Image Processing. pp. 115–116. Prentice Hall, Englewood Cliffs, NJ, (1986)

  26. Otsu N. (1979): A threshold selection method from gray-level histograms. IEEE trans. Syst. Man Cybern. 9(1): 62–66

    Article  MathSciNet  Google Scholar 

  27. Pal, U., Sarkar, A.: Recognition of printed urbu script. In: Proceedings of the 7th International Conference on Document Analysis and Recognition (ICDAR 2003)

  28. Pal U., Belaid A., Choisy Ch. (2003): Touching numeral segmentation using water reservoir concept. Pattern Recogn. Lett. 24, 261–272

    Article  Google Scholar 

  29. Pavlidis T. (1992): Algorithms for Graphics and Image Processing. Computer Science Press, Rockville MD

    Google Scholar 

  30. Plamondon P., Privitera CM. (1999): The segmentation of cursive handwritten: an approach based on off-line recovery of the motor-temporal information. IEEE Trans. Image Process. 8, 80–91

    Article  Google Scholar 

  31. Sauvola J., Pietikainen M. (2000): Adpative document image binarization. Pattern Recogn. 33: 225–236

    Article  Google Scholar 

  32. Shuyan Z., Zheru C., Penfei S., Hong Y. (2003): Two-stage segmentation of unconstrained handwritten Chinese characters. Pattern Recogn. 36: 145–156

    Article  MATH  Google Scholar 

  33. Simon, J.: Off-line cursive word recognition. In: Proc. IEEE 80, 1150–1161 (1992)

  34. Suen CY. (1993): Building a new generation of handwriting recognition systems. Pattern Recogn. Lett. 14, 303–315

    Article  Google Scholar 

  35. Ulmann J.R. (1969): Experiments with the n-tuple method of pattern recognition. IEEE Trans. Comput. 18(12): 1135–1137

    Google Scholar 

  36. Vinciarelli A. (2002): A survey on off-line cursive word recognition. Pattern Recogn. 35, 1433–1446

    Article  MATH  Google Scholar 

  37. Xiao, X., Leedham, G.: Cursive script segmentation incorporating Knowledge of writing. In: Proceedings of the 5th International Conference on Document Analysis and Recognition, pp. 535–538 (1999)

  38. Xia F. (2003): Normal vector and winding number in 2D digital images with their application for hole detection. Pattern Recogn. 36, 1383–1395

    Article  MATH  Google Scholar 

  39. Xu, Q., Lam, L., Suen, CY.: A knowledge-based segmentation system for handwritten dates on bank cheques. In: Sixth International Conference on Document Analysis and Recognition, ICDAR2001, pp. 384–388 (2001)

  40. Zhang, M., Suen, C.: Digital Image Processing, 2nd edn, pp. 398–402 (1987)

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Authors and Affiliations

  1. Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Research Center “Demokritos”, 153 10, Athens, Greece

    K. Ntzios, B. Gatos, I. Pratikakis, T. Konidaris & S. J. Perantonis

  2. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece

    K. Ntzios

Authors
  1. K. Ntzios
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  2. B. Gatos
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  3. I. Pratikakis
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  4. T. Konidaris
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  5. S. J. Perantonis
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Correspondence to K. Ntzios.

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Ntzios, K., Gatos, B., Pratikakis, I. et al. An old greek handwritten OCR system based on an efficient segmentation-free approach. IJDAR 9, 179–192 (2007). https://doi.org/10.1007/s10032-006-0031-z

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  • DOI: https://doi.org/10.1007/s10032-006-0031-z

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