Document Analysis Systems for Digital Libraries: Challenges and Opportunities

  • Henry S. Baird
  • Venugopal Govindaraju
  • Daniel P. Lopresti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3163)

Abstract

Implications of technical demands made within digital libraries (DL’s) for document image analysis systems are discussed. The state-of-the-art is summarized, including a digest of themes that emerged during the recent International Workshop on Document Image Analysis for Libraries. We attempt to specify, in considerable detail, the essential features of document analysis systems that can assist in: (a) the creation of DL’s; (b) automatic indexing and retrieval of doc-images within DL’s; (c) the presentation of doc-images to DL users; (d) navigation within and among doc-images in DL’s; and (e) effective use of personal and interactive DL’s.

References

  1. 1.
    Abdelazim, H.Y., Hashish, M.A.: Application of HMM to the recognition of isolated Arabic words. In: Proceedings of 11th National Computer Conference, pp. 761–774 (1989)Google Scholar
  2. 2.
    Association for Information and Image Management, International. 1100 Wayne Avenue, Suite 1100, Silver Spring, Maryland 20910; www.aiim.org
  3. 3.
    Allen, R.B., Schalow, J.: Metadata and data structures for the historical newspaper digital library. In: Proceedings of the 8th international conference on Information and knowledge management, pp. 147–153 (1999)Google Scholar
  4. 4.
    Amin, A.: Offline Arabic character recognition: The state of the art. Pattern Recognition 31(5), 517–530 (1997)CrossRefGoogle Scholar
  5. 5.
    Antonacopoulos, A., Karatzas, D.: Document image analysis for World War II personal records. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 336–341 (2004)Google Scholar
  6. 6.
    Baird, H.: Difficult and urgent open problems in document image analysis for libraries. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 25–32 (2004)Google Scholar
  7. 7.
    In: Baird, H.S., Govindaraju, V. (eds.): Proceedings of the International Workshop on Document Image Analysis for Libraries. IEEE Computer Society Press, Piscataway (2004)Google Scholar
  8. 8.
    Bansal, V.: Integrating knowledge sources in devanagari text recognition. IEEE Transactions on Systems, Man and Cybernetics Part A 30(4), 500–505 (2000)CrossRefGoogle Scholar
  9. 9.
    Barrett, W.: Digital mountain: From granite archive to global access. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 104–121 (2004)Google Scholar
  10. 10.
    Bazzi, I., Schwartz, R., Makhoul, J.: An omnifont open-vocabulary OCR system for English and Arabic. IEEE Pattern Analysis and Machine Intelligence 21(6), 495–504 (1999)CrossRefGoogle Scholar
  11. 11.
    Bray, T., Paoli, J., Sperberg-McQueen, C.M., Maler, E.: Extensible Markup Language (XML) 1.0, 2nd edn (2001)Google Scholar
  12. 12.
    Breuel, T.M., Janssen, W.C., Popat, K., Baird, H.S.: Paper to PDA. In: Proc., IAPR 16th ICPR, Quebec City, Canada, August 2002, vol. 4, pp. 476–479 (2002)Google Scholar
  13. 13.
    Chaudhuri, B.B., Pal, U.: An OCR system to read two Indian language scripts: Bangla and Devanagari. In: Proceedings of the 4th International Conference on Document Analysis and Recognition, pp. 1011–1015 (1997)Google Scholar
  14. 14.
    Chaudhuri, B.B., Pal, U., Mitra, M.: Automatic recognition of printed Oriya script. In: Proceedings of the 6th International Conference on Document Analysis and Recognition, pp. 795–799 (2001)Google Scholar
  15. 15.
    Chen, F.R., Bloomberg, D.: Summarization of imaged documents without OCR. Computer Vision and Image Understanding 70(3) (June 1998)Google Scholar
  16. 16.
    Chew, M., Baird, H.S.: BaffleText: a Human Interactive Proof. In: Proc., 10th IS&T/SPIE Document Recognition & Retrieval Conf., Santa Clara, CA, January 23–24 (2003)Google Scholar
  17. 17.
    Unicode Consortium. The Unicode Standard Version 4.0. Addison-Wesley (2003)Google Scholar
  18. 18.
    Couasnon, B., Camillerapp, J., Leplumey, I.: Making handwritten archives documents accessible to public with a generic system of document image analysis. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 270–277 (2004)Google Scholar
  19. 19.
    Dillon, A.: Reading from paper versus screens: A critical review of the empirical literature. Ergonomics 35(10), 1297–1326 (1992)CrossRefGoogle Scholar
  20. 20.
    Doermann, D.: The indexing and retrieval of document images: A survey. Computer Vision and Image Understanding 70(3) (June 1998)Google Scholar
  21. 21.
    E Ink, 733 Concord Avenue, Cambridge, MA 02138, www.eink.com
  22. 22.
    Govindaraju, V., Khedekar, S., Kompalli, S., Farooq, F., Setlur, S., Prasad, V.: Tools for enabling digital access to multilingual Indic documents. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 122–133 (2004)Google Scholar
  23. 23.
    Govindaraju, V., Xue, H.: Fast handwriting recognition for indexing historical documents. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 314–320 (2004)Google Scholar
  24. 24.
    Green, D.: The Java Tutorial: Internationalization, java.sun.com/docs/books/tutorial/i18n/
  25. 25.
    Gyricon Media, Inc., 6190 Jackson Road, Ann Arbor, MI 48103, www.gyriconmedia.com
  26. 26.
    Harit, G., Chadhury, S., Ghosh, H.: Managing document images in a digital library: An ontology guided approach. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 64–92 (2004)Google Scholar
  27. 27.
    Hochberg, J., Kerns, L., Kelly, P., Thomas, T.: Automatic script identification from images using cluster-based templates. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 378–381 (1995)Google Scholar
  28. 28.
    Hutchison, L., Barrett, W.A.: Fast registration of tabular document images using Fourier analysis. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 253–267 (2004)Google Scholar
  29. 29.
    JSTOR Digital Library. University of Michigan and Princeton University, www.jstor.org
  30. 30.
    Kim, D.H., Hwang, Y.S., Park, S.T., Kim, E., Paek, S., Bang, S.: Handwritten Korean character image database. In: Proceedings of the 2nd International Conference on Document Analysis and Recognition, pp. 470–473 (1993)Google Scholar
  31. 31.
    Kim, M., Jang, M., Choi, H., Rhee, T., Kim, J.H.: Digitalizing scheme of handwritten Hanja historical document. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 321–327 (2004)Google Scholar
  32. 32.
    Kompalli, S., Setlur, S., Govindaraju, V., Vemulapati, R.: Creation of data resources and design of an evaluation test bed for Devanagari script recognition. In: Proceedings of the 13th International Workshop on Research Issues on Data Engineering: Multi-lingual Information Management, pp. 55–61 (2003)Google Scholar
  33. 33.
    Kornfield, M., Manmatha, R., Allan, J.: Text alignment with handwritten documents. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 195–209 (2004)Google Scholar
  34. 34.
    Lavrenko, V., Rath, T., Manmatha, R.: Holistic word recognition for handwritten historical documents. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 278–287 (2004)Google Scholar
  35. 35.
    LeBourgeois, F., Trinh, E., Allier, B., Eglin, V., Emptoz, H.: Document images analysis solutions for digital libraries. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 2–24 (2004)Google Scholar
  36. 36.
    Lee, C.H., Kanungo, T.: The architecture of TrueViz: A groundTRUth / metadata editing and VIsualiZing toolkit. PR 36(3), 811–825 (2003)Google Scholar
  37. 37.
    Loce, R.P., Dougherty, E.R.: Enhancement and Restoration of Digital Documents: Statistical Design of Nonlinear Algorithms. Society of Photo-optical Instrumentation Engineers (January 1997) ISBN 081942109XGoogle Scholar
  38. 38.
    Ma, H., Doermann, D.: Adaptive Hindi OCR using generalized Hausdorff image comparison. ACM Transactions on Asian Language Information Processing 26(2), 198–213 (2003)Google Scholar
  39. 39.
    Marinai, S., Marino, E., Cesarani, F., Soda, G.: A general system for the retrieval of document images from digital libraries. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries. (DIAL 2004), pp. 150– 173 (2004)Google Scholar
  40. 40.
  41. 41.
    Nagy, G.: Twenty years of document image analysis in PAMI. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 38–62 (2000)CrossRefGoogle Scholar
  42. 42.
    Namboodiri, M., Jain, A.K.: On-line script recognition. In: Proceedings of the 16th International Conference on Pattern Recognition, pp. 736–739 (2002)Google Scholar
  43. 43.
    Bureau of Indian Standards. Indian script code for information interchange (1999) Google Scholar
  44. 44.
    Pal, U., Chaudhuri, B.B.: Script line separation from Indian multi-script documents. In: Proceedings of the 5th International Conference on Document Analysis and Recognition, pp. 406–409 (1999)Google Scholar
  45. 45.
    NPES/AIIM PDF – Archive Project, www.aiim.org/standards.asp?ID=25013
  46. 46.
    ProjectGutenberg, promo.net/pg
  47. 47.
    Rajkumar, S.: Indic typesetting – challenges and opportunities. TUGboat, 23(1) (2002)Google Scholar
  48. 48.
    Rice, S.V., Jenkins, F.R., Nartker, T.A.: The fifth annual test of OCR accuracy. Technical report, Information Science Research Institute, Univ. of Nevada at Las Vegas, Las Vegas, Nevada (1996) ISRI TR-96-01Google Scholar
  49. 49.
    Sarkar, P., Baird, H.S., Henderson, J.: Triage of OCR output using ’confidence’ scores. In: Proc. 9th IS&T/SPIE Document Recognition & Retrieval Conf., San Jose, CA (January 2002)Google Scholar
  50. 50.
    Sellen, J., Harper, R.H.R.: The Myth of the Paperless Office. The MIT Press, Cambridge (2002)Google Scholar
  51. 51.
    Setlur, S., Lawson, A., Govindaraju, V., Srihari, S.: Large scale address recognition systems – truthing, testing, tools and other evaluation issues. International Journal of Document Analysis and Recognition 4(3), 154–169 (2002)CrossRefGoogle Scholar
  52. 52.
    Shanbhag, S., Rao, D., Joshi, R.K.: An intelligent multi-layered input scheme for phonetic scripts. In: Proceedings of the 2nd International Symposium on Smart Graphics, pp. 35–38 (2002)Google Scholar
  53. 53.
    Simske, S.J., Sturgill, M.: A ground-truthing engine for proofsetting, publishing, re-purposing and quality assurance. In: Proceedings of the 2003 ACM Symposium on Document Engineering, pp. 150–152 (2003)Google Scholar
  54. 54.
    Sin, B.K., Kim, J.H.: Ligature modeling for online cursive script recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(6), 623–633 (1997)CrossRefGoogle Scholar
  55. 55.
    Barney Smith, E.H., Qiu, X.: Relating statistical image differences and degradation features. In: Lopresti, D.P., Hu, J., Kashi, R.S. (eds.) DAS 2002. LNCS, vol. 2423, pp. 1–12. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  56. 56.
    Spitz, L.: SPAM: A scientific paper access method. In: Document Analysis Systems II, pp. 242–255. World Scientific, Singapore (1998)CrossRefGoogle Scholar
  57. 57.
    Srihari, S.N., Kuebert, E.J.: Integration of handwritten address interpretation technology into the United States Postal Service remote computer reader system. In: Proceedings of the 4th International Conference on Document Analysis and Recognition (1997)Google Scholar
  58. 58.
    Stork, D.G.: The Open Mind initiative. In: Proc., IEEE Expert Systems and Their Applications, pp. 16–20 (May/June 1999), www.openmind.org
  59. 59.
    Summers, K.: Document image improvement for OCR as a classification problem. In: Proc., SPIE/IS&T Electronic Imaging Conf. on Document Recognition & Retrieval X, Santa Clara, CA, January 2003, vol. 5010, pp. 73–83. SPIE, San Jose (2003)Google Scholar
  60. 60.
    Sun Solaris 9 operating system features and benefits – compatibility, www.sun.com/software/solaris/sparc/solaris9featurescompatibility.html
  61. 61.
    Tai, J.-W., Liu, Y.-J., Zhang, L.-Q.: A model based detecting approach for feature extraction of off-line handwritten Chinese character recognition. In: Proceedings of the 2nd International Conference on Document Analysis and Recognition (1993)Google Scholar
  62. 62.
    Tan, T.N.: Rotation invariant texture features and their use in automatic script identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(7), 751–756 (1998)CrossRefGoogle Scholar
  63. 63.
    The XML version of the TEI guidelines, www.tei-c.org/P4X/CH.html
  64. 64.
    Wang, A.-B., Huang, J.S., Fan, K.-C.: Optical recognition of handwritten chinese characters by partial matching. In: Proceedings of the 2nd International Conference on Document Analysis and Recognition (1993)Google Scholar
  65. 65.
    Yosef, B., Kedem, K., Dinstein, I., Beit-Arie, M., Engel, E.: Classification of Hebrew calligraphic handwriting styles: Preliminary results. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 299–305 (2004)Google Scholar
  66. 66.
    Zhang, B., Tomai, C., Srihari, S., Govindaraju, V.: Construction of handwriting databases using transcript-based mapping. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 288–298 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Henry S. Baird
    • 1
  • Venugopal Govindaraju
    • 2
  • Daniel P. Lopresti
    • 1
  1. 1.CSE DepartmentLehigh UniversityBethlehemUSA
  2. 2.CEDARUniversity at Buffalo, SUNYBuffaloUSA

Personalised recommendations