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

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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.

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References

  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)

    Article  Google Scholar 

  2. Bansal V., Sinha R.: Integrating knowledge sources in Devanagari text recognition. IEEE Trans. Syst. Man Cybern. A 30(4), 500–505 (2000)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  4. Bansal V., Sinha R.: Segmentation of touching and fused Devanagari characters. Pattern Recognit. 35, 875–893 (2002)

    Article  MATH  Google Scholar 

  5. Bansal V., Sinha R.: Segmentation of touching and fused Devanagari characters. Pattern Recognit. 35, 875–893 (2002)

    Article  MATH  Google Scholar 

  6. Bishop C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1996)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  8. Casey R., Lecolinet E.: A survey of methods and strategies in character segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 690–706 (1996)

    Article  Google Scholar 

  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)

  10. Christopher M., Hinrich S.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  11. Daniels P.T., Bright W.: The World’s Writing Systems. Oxford University Press, New York (1996)

    Google Scholar 

  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)

  13. Duda R.O., Hart P.E., Stork D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  20. The cedar-ilt data set. http://www.cedar.buffalo.edu/ilt/

  21. Juan C.A., Enrique V.: Efficient error-correcting viterbi parsing. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1109–1116 (1998)

    Article  Google Scholar 

  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)

  23. Kim G., Govindaraju V., Srihari S.N.: An architecture for handwritten text recognition systems. IJDAR 2, 37–44 (1999)

    Article  Google Scholar 

  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)

  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)

  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)

  27. Kukich K.: Techniques for automatically correcting words in text. ACM Comput. Surv. 24(4), 377–439 (1992)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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. Mitchell T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  33. Ohala, M.: Aspects of Hindi Phonology. Motilal Banarasidas, Delhi (1983). ISBN: 0895811162.

  34. Rocha J., Pavlidis T.: Character recognition without segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 17, 903–909 (1995)

    Article  Google Scholar 

  35. Rosenfeld R.: A maximum entropy approach to adaptive statistical language modeling. Comput. Speech Lang. 10, 187–228 (1996)

    Article  Google Scholar 

  36. Sinha R.: Plang: a picture language schema for a class of pictures. Pattern Recognit. 16(4), 373–383 (1983)

    Article  Google Scholar 

  37. Sinha R.: Rule based contextual post-processing for devanagari text recognition. Pattern Recognit. 20, 475–485 (1987)

    Article  Google Scholar 

  38. Sinha R., Mahabala H.: Machine recognition of Devnagari script. IEEE Trans. Syst. Man Cybern. 9, 435–441 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  40. Slavik, P., Govindaraju, V.: An overview of run-length encoding of handwritten word images. Technical report, SUNY, Buffalo (2000)

  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)

    Article  Google Scholar 

  42. Sonka M., Hlavac V., Boyle R.: Image Processing, Analysis and Machine Vision, 2nd edn. Brooks-Cole, Belmont (1999)

    Google Scholar 

  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 2001

  44. Wu Y., Ianakiev K.G., Govindaraju V.: Improved k-nearest neighbor classification. Pattern Recognit. 35, 2311–2318 (2002)

    Article  MATH  Google Scholar 

  45. Xue, H.: Stochastic Modeling of High-Level Structures in Handwriting Recognition. PhD thesis, University at Buffalo, The State University of New York (2002)

  46. Yu B., Jain A.: A generic system for form dropout. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1127–1134 (1996)

    Article  Google Scholar 

  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 1997

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University at Buffalo, State University of New York, Buffalo, USA

    Suryaprakash Kompalli, Srirangaraj Setlur & Venu Govindaraju

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  1. Suryaprakash Kompalli
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  2. Srirangaraj Setlur
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  3. Venu Govindaraju
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Correspondence to Srirangaraj Setlur.

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Kompalli, S., Setlur, S. & Govindaraju, V. Devanagari OCR using a recognition driven segmentation framework and stochastic language models. IJDAR 12, 123–138 (2009). https://doi.org/10.1007/s10032-009-0086-8

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  • DOI: https://doi.org/10.1007/s10032-009-0086-8

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