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A Complete OCR System for Tamil Magazine Documents

  • Aparna Kokku
  • Srinivasa Chakravarthy
Chapter
Part of the Advances in Pattern Recognition book series (ACVPR)

Abstract

We present a complete optical character recognition (OCR) system for Tamil magazines/documents. All the standard elements of OCR process like de-skewing, preprocessing, segmentation, character recognition, and reconstruction are implemented. Experience with OCR problems teaches that for most subtasks of OCR, there is no single technique that gives perfect results for every type of document image. We exploit the ability of neural networks to learn from experience in solving the problems of segmentation and character recognition. Text segmentation of Tamil newsprint poses a new challenge owing to its italic-like font type; problems that arise in recognition of touching and close characters are discussed. Character recognition efficiency varied from 94 to 97% for this type of font. The grouping of blocks into logical units and the determination of reading order within each logical unit helped us in reconstructing automatically the document image in an editable format.

Keywords

Tamil OCR Neural networks De-skewing Segmentation Gabor features 

References

  1. 1.
    Nagy, G.: Twenty years of document image analysis in PAMI. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 2000, pp. 38–62.CrossRefGoogle Scholar
  2. 2.
    Chaudhuri, B.B. and Pal, U.: An OCR system to read two Indian language scripts: Bangla and Devanagari (Hindi). Proceedings of Intl’ Conference on Document Analysis and Recognition, Ulm, Germany, pp. 1011–1015, 1997.Google Scholar
  3. 3.
    Rajasekharan, S.N.S. and Deekshatulu, B.L.: Generation and recognition of printed Telugu characters, Computer Graphics and Image Processing 6, 1977, pp. 335–360.CrossRefGoogle Scholar
  4. 4.
    Bagdanov, A. and Kanai, J.: Projection profile based Skew estimation Algorithm for JBIG compressed images. Proceedings of Intl’ Conference on Document Analysis and Recognition, Ulm, Germany, 1997, pp. 401–405.Google Scholar
  5. 5.
    Srihari, S.N. and Govindaraju, V.: Analysis of textual images using the hough transform. Machine Vision and Applications, 2(3), 1989, pp. 141–153.CrossRefGoogle Scholar
  6. 6.
    Pal, U. and Chaudhuri, B.B.: An improved document skew angle estimation technique. Pattern Recognition Letters, 17(8), 1996, pp. 899–904.CrossRefGoogle Scholar
  7. 7.
    Yu, B. and Jain, A.K.: A robust and fast skew detection algorithm for generic documents. Pattern Recognition, 29(10), 1996, pp. 1599–1629.CrossRefGoogle Scholar
  8. 8.
    Hashizume, A., Yeh, P.S. and Rosenfeld, A.: A method of detecting the orientation of aligned components. Pattern Recognition Letters, 4, 1986, pp. 125–132.CrossRefGoogle Scholar
  9. 9.
    O’Gorman, L.: The document spectrum for page layout analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1993, pp. 1162–1173.CrossRefGoogle Scholar
  10. 10.
    Chaudhuri, B.B. and Pal, U.: A complete printed Bangla OCR system. Pattern Recognition, 31(5), 1998, pp. 531–549.CrossRefGoogle Scholar
  11. 11.
    Yan, H.: Skew correction of document images using interline cross correlation. CVGIP: Graphical Models and Image Processing, 55(6), 1993, pp. 538–543.CrossRefGoogle Scholar
  12. 12.
    Chen, S. and Haralick, R.M.: An automatic algorithm for Text Skew estimation in document images using recursive morphological transforms. Proceedings of First IEEE International Conference on Image Processing, 1994, pp. 139–143, Austin, Texas.Google Scholar
  13. 13.
    Chen, S. and Haralick, R.M.: Recursive erosion, dilation, opening and closing transforms. IEEE Transaction on Image Processing, 4(3),1995, pp. 335–345.CrossRefGoogle Scholar
  14. 14.
    Aghajan, H.K. and Kailath, T.: SLIDE: Subspace-Based line detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 16(11), 1994, pp. 1057–1073.CrossRefGoogle Scholar
  15. 15.
    Ostu, N.: A threshold selection method from gray scale Histograms. IEEE Transactions on Systems Man Cybernet, 8, 1979, pp. 62–66.Google Scholar
  16. 16.
    Liu, Y. and Srihari, S.N.: Document image binarization based on texture features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5), 1997, pp. 540–544.CrossRefGoogle Scholar
  17. 17.
    Trier, O.D. and Taxt, T.: Evaluation of binarization methods for document images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(3), 1995, pp. 312–315.CrossRefGoogle Scholar
  18. 18.
    Abak, A.T., Baris, U., and. Sankur, B.: The performance evaluation of thresholding algorithms for optical character recognition. Proceedings of 4th International Conference on Document Analysis and Recognition, 1997, pp. 697–700, Ulm, GermanyGoogle Scholar
  19. 19.
    Wong, K.J., Casey, R.G., and Wahl, F.M.: Document analysis system. IBM Journal of Research and Development, 26(6) 1982, pp. 647–656.CrossRefGoogle Scholar
  20. 20.
    Wang, D. and Srihari, S.N.: Classification of newspaper image blocks using texture analysis. Computer Vision, Graphics and Image Processing 47, 1989, pp. 327–352.CrossRefGoogle Scholar
  21. 21.
    Nagy, G., Seth, S., and Viswanathan, M.: A prototype document image analysis system for technical journals. IEEE Computer, 25(7), 1992, pp. 10–22.Google Scholar
  22. 22.
    Krishnamoorthy, M., Nagy, G., Seth, S., and Viswanathan, M.: Syntactic segmentation and labeling of digitized pages from technical journals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(7), 1993, pp. 737–747.CrossRefGoogle Scholar
  23. 23.
    Pavlidis, T. and Zhou, J.: Page segmentation and classification. CVGIP 54(6), 1992, pp. 484–496.Google Scholar
  24. 24.
    Jain, A. K. and Yu, B.: Document representation and its application to page decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 1998, pp. 294–307.CrossRefGoogle Scholar
  25. 25.
    Jain, A.K. and Bhattacharjee, S.: Text segmentation using Gabor filters for automatic document processing. Machine Vision and Applications, 5(3), 1992, pp. 169–184.CrossRefGoogle Scholar
  26. 26.
    Le, D.X., Thoma, G.R., and Wechsler, H.: Classification of binary document images into textual or nontextual data blocks using neural network models. Machine Vision and Applications, 8, 1995, pp. 289–304.CrossRefGoogle Scholar
  27. 27.
    Casey, R.G. and Lecolinet, E.: A survey of methods and strategies in character segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 1996, pp. 690–706.CrossRefGoogle Scholar
  28. 28.
    Lu, Y.: Machine printed character recognition – An overview. Pattern Recognition, 28(1), 1995, pp. 67–80.CrossRefGoogle Scholar
  29. 29.
    Tsujimoto, S. and Asada, H.: Resolving ambiguity in segmenting touching characters. Proceedings of First International Conference on Document Analysis and Recognition, 1991, pp. 701–709.Google Scholar
  30. 30.
    Hoffman, R.L. and McCullough, J.W.: Segmentation methods for recognition of machine printed characters. IBM Journal of Research and Development, 1971, pp. 153–165.Google Scholar
  31. 31.
    Wang, J., and Jean, J.: Segmentation of merged characters by neural networks and shortest path. Pattern recognition, 27(5), 1994, pp. 649–658.CrossRefGoogle Scholar
  32. 32.
    Mori, S., Suen, C.Y. and Yamamoto, K.: Historical review of OCR research and development. Proceedings of IEEE, 80(7), 1992, pp. 1029–1058.CrossRefGoogle Scholar
  33. 33.
    Lee, S.W. and Kim, Y.J.: Direct extraction of topographic features for gray scale character recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(7), 1995, pp. 724–729.CrossRefGoogle Scholar
  34. 34.
    Lee, S.W., Lee, D.J., and Park, H.S.: A new methodology for gray-scale character segmentation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10), 1996, pp. 1045–1050.CrossRefGoogle Scholar
  35. 35.
    Siromoney, G., Chandrasekaran, R., and Chandrasekaran, M.: Machine recognition of printed Tamil characters. Pattern Recognition, 10, 1978, pp. 243–247.zbMATHCrossRefGoogle Scholar
  36. 36.
    Sinha, R.M.K., and Mahabala, H.: Machine recognition of Devanagari script. IEEE Transactions on Systems Man Cybernet, SMC-9, 1979, pp. 435–441.Google Scholar
  37. 37.
    Sinha, R.M.K.: Rule based contextual post-processing for Devanagiri text recognition. Pattern Recognition, 20, 1987, pp. 475–485.CrossRefGoogle Scholar
  38. 38.
    Tsujimoto, S. and Asada, H.: Major components of a complete text reading system. Proceedings of IEEE, 80(7), 1992, pp. 1133–1149.CrossRefGoogle Scholar
  39. 39.
    Niyogi, D. and Srihari, S.N.: Knowledge-based derivation of document logical structure. International Conference on Document Analysis and Recognition, 1995, pp. 472–475, Montreal, CanadaCrossRefGoogle Scholar
  40. 40.
    Moody, J.E. and Darken, C.J.: Fast learning in networks of locally tuned processing units. Neural Computation 1, 1989, pp. 281–294.CrossRefGoogle Scholar
  41. 41.
    Haykin, S.: Neural networks: A comprehensive foundation. Prentice Hall, 1999Google Scholar
  42. 42.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortial filters. Journal of the Optical Society of America A, 2(7), 1985, pp. 1160–1169.CrossRefGoogle Scholar
  43. 43.
    Aparna, H.K.: “Document image analysis: A complete OCR system development for Tamil magazine documents”, M.S. Thesis, Department of Electrical Engineering, Indian Institute of Technology, May, 2003, Madras.Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Aparna Kokku
    • 1
  • Srinivasa Chakravarthy
    • 1
  1. 1.Department of BiotechnologyIIT-MadrasChennaiIndia

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