Advertisement

Devanagari OCR using a recognition driven segmentation framework and stochastic language models

  • Suryaprakash Kompalli
  • Srirangaraj SetlurEmail author
  • Venu Govindaraju
Original Paper

Abstract

This paper describes a novel recognition driven segmentation methodology for Devanagari Optical Character Recognition. Prior approaches have used sequential rules to segment characters followed by template matching for classification. Our method uses a graph representation to segment characters. This method allows us to segment horizontally or vertically overlapping characters as well as those connected along non-linear boundaries into finer primitive components. The components are then processed by a classifier and the classifier score is used to determine if the components need to be further segmented. Multiple hypotheses are obtained for each composite character by considering all possible combinations of the classifier results for the primitive components. Word recognition is performed by designing a stochastic finite state automaton (SFSA) that takes into account both classifier scores as well as character frequencies. A novel feature of our approach is that we use sub-character primitive components in the classification stage in order to reduce the number of classes whereas we use an n-gram language model based on the linguistic character units for word recognition.

Keywords

Word Recognition Optical Character Recognition Handwriting Recognition Word Image False Reject Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baker P., Hardie A., McEnery T., Xiao R., Bontcheva K., Cunningham H., Gaizauskas R., Hamza O., Maynard D., Tablan V., Ursu C., Jayaram B., Leisher M.: Corpus linguistics and south asian languages: corpus creation and tool development. Lit. Linguist. Comput. 19(4), 509–524 (2004)CrossRefGoogle Scholar
  2. 2.
    Bansal V., Sinha R.: Integrating knowledge sources in Devanagari text recognition. IEEE Trans. Syst. Man Cybern. A 30(4), 500–505 (2000)CrossRefGoogle Scholar
  3. 3.
    Bansal V., Sinha R.: Partitioning and searching dictionary for correction of optically-read devanagari character strings. Int. J. Doc. Anal. Recognit. 4(4), 269–280 (2002)CrossRefGoogle Scholar
  4. 4.
    Bansal V., Sinha R.: Segmentation of touching and fused Devanagari characters. Pattern Recognit. 35, 875–893 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Bansal V., Sinha R.: Segmentation of touching and fused Devanagari characters. Pattern Recognit. 35, 875–893 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bishop C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1996)zbMATHGoogle Scholar
  7. 7.
    Bouchaffra D., Govindaraju V., Srihari S.N.: Postprocessing of recognized strings using nonstationary markovian models. IEEE Trans. Pattern Anal. Mach. Intell. 21(10), 990–999 (1999)CrossRefGoogle Scholar
  8. 8.
    Casey R., Lecolinet E.: A survey of methods and strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 690–706 (1996)CrossRefGoogle Scholar
  9. 9.
    Chaudhuri, 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
  10. 10.
    Christopher M., Hinrich S.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  11. 11.
    Daniels P.T., Bright W.: The World’s Writing Systems. Oxford University Press, New York (1996)Google Scholar
  12. 12.
    Ding, X., Wen, D., Peng, L., Liu, C.: Document digitization technology and its application for digital library in china. In: Proceedings of the 1st International Workshop on Document Image Analysis for Libraries (DIAL 2004), pp. 46–53 (2004)Google Scholar
  13. 13.
    Duda R.O., Hart P.E., Stork D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)Google Scholar
  14. 14.
    Favata J., Srikantan G.: A multiple feature/resolution approach to handprinted digit and character recognition. Int. J. Imaging Syst. Technol. 7, 304–311 (1996)CrossRefGoogle Scholar
  15. 15.
    Forcada, M.: Corpus-based stochastic finite-state predictive text entry for reduced keyboards: application to catalan. In: Procesamiento del Lenguaje Natural, pp. 65–70 (2001)Google Scholar
  16. 16.
    Garain U., Chaudhuri B.: Segmentation of touching characters in printed devnagari and bangla scripts using fuzzy multi- factorial analysis. IEEE Trans. Syst. Man. Cybern. C 32(4), 449–459 (2002)CrossRefGoogle Scholar
  17. 17.
    Govindaraju, V., Khedekar, S., Kompalli, S., Farooq, F., Setlur, S., Vemulapati, R.: 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
  18. 18.
    Hirsimaki, T., Creutz, M., Siivola, V., Mikko, K.: Morphologically motivated language models in speech recognition. In: Proceedings of International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, pp. 121–126 (2005)Google Scholar
  19. 19.
    Hull J.J., Srihari S.N.: Experiments in text recognition with binary n-grams and viterbi algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 4(5), 520–530 (1982)CrossRefGoogle Scholar
  20. 20.
    The cedar-ilt data set. http://www.cedar.buffalo.edu/ilt/
  21. 21.
    Juan C.A., Enrique V.: Efficient error-correcting viterbi parsing. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1109–1116 (1998)CrossRefGoogle Scholar
  22. 22.
    Juan, C.P.-C., Juan, C.A., Rafael, L.: Stochastic error-correcting parsing for OCR post-processing. In: Proceedings of the 15th International Conference on Pattern Recognition, vol. 4, pp. 405–408 (2000)Google Scholar
  23. 23.
    Kim G., Govindaraju V., Srihari S.N.: An architecture for handwritten text recognition systems. IJDAR 2, 37–44 (1999)CrossRefGoogle Scholar
  24. 24.
    Kompalli, S., Nayak, S., Setlur, S., Govindaraju, V.: Challenges in ocr of devanagari documents. In: Proceedings of the 8th International Conference on Document Analysis and Recognition, pp. 327–333 (2005)Google Scholar
  25. 25.
    Kompalli, S., Setlur, S., Govindaraju, V.: Design and comparison of segmentation driven and recognition driven Devanagari ocr. In: Proceedings of the 2nd International Conference on Document Image Analysis for Libraries, pp. 96–102 (2006)Google Scholar
  26. 26.
    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
  27. 27.
    Kukich K.: Techniques for automatically correcting words in text. ACM Comput. Surv. 24(4), 377–439 (1992)CrossRefGoogle Scholar
  28. 28.
    Kunihio, F., Imagawa, T., Ashida, E.: Character recognition with selective attention. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 593–598 (1991)Google Scholar
  29. 29.
    Lee S.-W., Lee D.-J., Park H.-S.: A new methodology for gray-scale character segmentation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1045–1050 (1996)CrossRefGoogle Scholar
  30. 30.
    Ma H., Doermann D.: Adaptive hindi OCR using generalized hausdorff image comparison. ACM Trans. Asian Lang. Inf. Process. 26(2), 198–213 (2003)Google Scholar
  31. 31.
    Mitchell T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  32. 32.
    Mori S., Suen C.Y., Yamamoto K.: Historical review of OCR research and development. Proc. IEEE 80, 1029–1058 (1992)CrossRefGoogle Scholar
  33. 33.
    Ohala, M.: Aspects of Hindi Phonology. Motilal Banarasidas, Delhi (1983). ISBN: 0895811162.Google Scholar
  34. 34.
    Rocha J., Pavlidis T.: Character recognition without segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 17, 903–909 (1995)CrossRefGoogle Scholar
  35. 35.
    Rosenfeld R.: A maximum entropy approach to adaptive statistical language modeling. Comput. Speech Lang. 10, 187–228 (1996)CrossRefGoogle Scholar
  36. 36.
    Sinha R.: Plang: a picture language schema for a class of pictures. Pattern Recognit. 16(4), 373–383 (1983)CrossRefGoogle Scholar
  37. 37.
    Sinha R.: Rule based contextual post-processing for devanagari text recognition. Pattern Recognit. 20, 475–485 (1987)CrossRefGoogle Scholar
  38. 38.
    Sinha R., Mahabala H.: Machine recognition of Devnagari script. IEEE Trans. Syst. Man Cybern. 9, 435–441 (1979)zbMATHCrossRefMathSciNetGoogle Scholar
  39. 39.
    Sinha R., Prasada B., Houle G., Sabourin M.: Hybrid contextural text recognition with string matching. IEEE Trans. Pattern Anal. Mach. Intell. 15, 915–925 (1993)CrossRefGoogle Scholar
  40. 40.
    Slavik, P., Govindaraju, V.: An overview of run-length encoding of handwritten word images. Technical report, SUNY, Buffalo (2000)Google Scholar
  41. 41.
    Song J., Li Z., Lyu M., Cai S.: Recognition of merged characters based on forepart prediction, necessity-sufficiency matching, and character-adaptive masking. IEEE Trans. Syst. Man Cybern. B 35, 2–11 (2005)CrossRefGoogle Scholar
  42. 42.
    Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision, 2nd edn. Brooks-Cole, Belmont (1999)Google Scholar
  43. 43.
    Woo, K.J., George, T.R.: Automated labeling in document images. In: Proceedings of the SPIE, Document Recognition and Retrieval VIII, vol. 4307, pp. 111–122, January 2001Google Scholar
  44. 44.
    Wu Y., Ianakiev K.G., Govindaraju V.: Improved k-nearest neighbor classification. Pattern Recognit. 35, 2311–2318 (2002)zbMATHCrossRefGoogle Scholar
  45. 45.
    Xue, H.: Stochastic Modeling of High-Level Structures in Handwriting Recognition. PhD thesis, University at Buffalo, The State University of New York (2002)Google Scholar
  46. 46.
    Yu B., Jain A.: A generic system for form dropout. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1127–1134 (1996)CrossRefGoogle Scholar
  47. 47.
    Zheng, J., Ding, X., Wu, Y.: Recognizing on-line handwritten chinese character via farg matching. In: Proceedings of the 4th International Conference on Document Analysis and Recognition, vol. 2, pp. 621–624, August 1997Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Suryaprakash Kompalli
    • 1
  • Srirangaraj Setlur
    • 1
    Email author
  • Venu Govindaraju
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity at Buffalo, State University of New YorkBuffaloUSA

Personalised recommendations