A data acquisition and analysis system for palm leaf documents in Telugu

ABSTRACT

This paper briefly reviews the progress in the field of hand written character recognition (HWCR) applied to the Indian languages with a special emphasis on the palm leaf character recognition (PLCR) techniques. The various methodologies and techniques for character recognition (CR) have been discussed in the paper. HWCR applied to historical documents like Palm leaves and old hand written manuscripts is much more challenging due to the limited progress in this area. These documents containing texts and treaties on a host of subjects are of both national and historical importance. Characters on the palm leaf have the additional properties like depth, an added feature which can be gainfully exploited during Palm Leaf Character Recognition (PLCR). The unique method of data collection initiated with isolated Telugu characters from palm leaf manuscripts, and the building of the palm leaf character database is described in this paper. A comparative analysis of the results for PLCR obtained by various techniques are also presented.

References

1. V. Aradhya, G. Hemantha Kumar, and S. Noushath. Robust unconstrained handwritten digit recognition using radon transform. In IEEE - ICSCN., pages 626--629, 2007.
2. H. Avi-Itzhak, T. Diep, and H. Gartland. High accuracy optical character recognition using neural network with centroid dithering. IEEE Trans. Pattern Anal. Machine Intell., 17: 218--228, Feb. 1995.
3. V. Babu, L. Prasanth, R. Sharma, and A. Bharath. HMM-based online handwriting recognition system for telugu symbols. In Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, volume 1, pages 63--67, sept. 2007.
4. I. Bazzi, R. Schwartz, and J. Makhoul. An omnifont open vocabulary ocr system for english and arabic. IEEE Trans. Pattern Anal. Machine Intell., 21: 495--504, June 1999.
5. A. Belaid and J. P. Haton. A syntactic approach for handwritten mathematical formula recognition. IEEE Trans. Pattern Anal. Machine Intell., PAMI-6: 105--111, 1984.
6. U. Bhattacharya and B. Chaudhuri. Handwritten numeral databases of indian scripts and multistage recognition of mixed numerals. IEEE transcations on pattern analysis and machine intelligence, 31(3): 444--457, Mar. 2009.
7. R. M. Bozinovic and S. N. Srihari. Off-line cursive script word recognition. IEEE Trans. Pattern Anal. Machine Intell., 11: 68--83, Jan. 1989.
8. L. Earnest. Machine reading of cursive script. In Proc. IFIP Congr., pages 462--466, 1963.
9. El-Sheikh and R. Guindi. Computer recognition of arabic cursive scripts. Pattern Recognit., 21(4): 293--302, 1988.
10. S. Espana-Boquera, M. J. Castro-Bleda, J. Gorbe-Moya, and F. Zamora-Martinez. Improving offline handwritten text recognition with hybrid HMM/ANN models. IEEE Trans. on PAMI, 33(4): 767--779, April 2011.
11. V. Govindan and A. Shivaprasad. Character recognition -- a review. IEEE Trans. Pattern Anal. Machine Intell., 23(7): 671--683, 1990.
12. J. Hu, S. Lim, and M. Brown. Writer independent on-line handwriting recognition using an hmm approach. Pattern Recognition, 33: 133--147, 2000.
13. J. John, K. Pramod, and K. Balakrishnan. Handwritten character recognition of south scripts: A review. In National Conference on Indian Language Computing., pages 469--487, 2011.
14. Kannan and Prabhakar. An improved handwritten tamil character recognition system using octal graph. Journal of Computer Science, 41(7): 509--516, 2008.
15. C. V. Lakshmi, R. Jain, and C. Patvardhan. Handwritten devnagari numerals recognition with higher accuracy. In IEEE International Conference on Computational Intelligence and Multimedia Applications, pages 255--259, 2007.
16. L. Lam, S. Lee, and C. Suen. Thinning methodologies--a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14: 869--885, Sept. 1992.
17. J. Mantas. An overview of character recognition methodologies. Pattern Recognit., 19(6): 425--430, 1986.
18. S. Mori, C. Suen, and K. Yamamoto. Historical review of OCR, research and development. Proc. IEEE, 80: 469--487, July 1992.
19. S. Mori, K. Yamamoto, and M. Yasuda. Research on machine recognition of handprinted characters. IEEE Trans. Pattern Anal. Machine Intell., 6: 386--404, Apr. 1984.
20. V. N. Murthy and A. Ramakrishnan. Choice of classifiers in hierarchical recognition of online handwritten kannada and tamil aksharas. Journal of Universal Computer Science, 17: 94--106, 2011.
21. I. Oh, J. Lee, and C. Suen. Analysis of class separation and combination of class-dependent features for handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21: 1089--1094, Oct. 1999.
22. U. Pal and B. Chaudhuri. Indian script character recognition: a survey. Pattern Recognition, 37(9): 1887--1899, 2004.
23. U. Pal, R. Jayadevan, and N. Sharma. Handwriting recognition in indian regional scripts: A survey of offline techniques. 11(1): 1:1--1:35, Mar. 2012.
24. P. B. Pati and A. Ramakrishnan. Ocr in indian scripts: A survey. IETE Technical Review, 22(3): 217--227, 2005.
25. P. B. Pati and A. Ramakrishnan. Word level multi-script identification. Pattern Recognition Letters, 29: 1218--1229, 2008.
26. R. Plamondon, D. P. Lopresti, L. R. B. Schomaker, and R. Srihari. On-line handwriting recognition. Encyclopedia of Electrical and Electronics Engineering: Eds. New York: Wiley, 15: 123--146, 1999.
27. L. Prasanth, V. Jagadeesh Babu, R. Raghunath Sharma, G. Prabhakara Rao, and M. Dinesh. Elastic matching of online handwritten tamil and telugu scripts using local features. In Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, volume 2, pages 1028--1032, sept. 2007.
28. S. Rajashekararadhya and P. V. Ranjan. Neural network based handwritten numeral recognition of kannada and telugu scripts. In IEEE TENCON, 2008.
29. P. N. Sastry and R. Krishnan. Isolated telugu palm leaf character recognition using radon transform--a novel approach. In IEEE-WICT, 2012.
30. P. N. Sastry, R. Krishnan, and T. Rajinikanth. Palm leaf telugu character recognition using hough transform. In Proceedings of International Conference on Advanced Computing Methodologies (ICACM-2011), pages 21--28, Dec 2011.
31. P. N. Sastry, R. Krishnan, and B. V. Sanker Ram. Telugu character recognition on palm leaves-a three dimensional approach technology. Spectrum (JNTU Hyderabad), 2(3): 19--26, Nov. 2008.
32. P. N. Sastry, R. Krishnan, and B. V. Sanker Ram. Telugu character recognition on palm leaves-a three dimensional approach technology. ARPN Journal of Engineering and Applied Sciences, 5(3), Mar. 2010.
33. A. Sen, G. Ananthakrishnan, S. Sundaram, and A. Ramakrishnan. Dynamic space warping of strokes for recognition of online handwritten characters. International Journal of Pattern Recognition and Artificial Intelligence, 23(5): 925--943, 2009.
34. Senior and Robinson. An off-line cursive handwriting recognition system. IEEE Transactions on Pattern analysis and Machine Intelligence, 20(3): 309--321, 1998.
35. M. Shridhar and A. Badreldin. High accuracy syntactic recognition algorithm for handwritten numerals. IEEE Trans. Syst., Man, Cybern., SMC-15: 152--158, 1985.
36. C. Suen, M. Berthod, and S. Mori. Automatic recognition of handprinted characters--the state of the art. In Proc. IEEE., pages 469--487, 1980.
37. C. Suen, C. Nadal, R. Legault, T. Mai, and L. Lam. Computer recognition of unconstrained handwritten numerals. Special Issue of Proc. Of the IECC, 14(4): 1162--1180, Aug. 1992.
38. C. Suen, C. Tappert, and T. Wakahara. The state of the art in on-line handwriting recognition. IEEE Trans. Pattern Anal. Machine Intell., 12(5): 787--808, Aug. 1990.
39. O. Surinta and R. Chamchong. Image segmentation of historical handwriting from palm leaf manuscripts. In 5th IFIP International Conference on Intelligent Information Processing, pages 182--189, 2008.
40. Sutha and Ramaraj. Neural network based offline tamil handwritten character recognition system. In International Conference on Computational Intelligence and Multimedia Applications, pages 446--450, 2007.
41. TDIL. Technology development for indian languages. http://tdil.mit.gov.in/.
42. Q. Tian, P. Zhang, T. Alexander, and Y. Kim. Survey: Omnifont printed character recognition. Visual Commun. Image Process: Image Process., pages 260--268, 1991.
43. Y. D. Trier and A. K. Jain. Goal directed evaluation of binarization methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17: 1191--1201, Dec. 1995.
44. S. V. N. Manjunath Aradhya, G. Hemanth Kumar. Multilingual ocr system for south indian scripts and english documents. In 5th IFIP International Conference on Intelligent Information Processing, pages 658--668, 2008.
45. S. Zhixin, S. Srirangaraj, and V. Govindaraju. Digital enhancement of palm leaf manuscript images using normalization techniques. In International Conference On Knowledge Based Computer Systems, pages 19--22, Dec. 2004.

Index Terms

• Applied computing

  • Document management and text processing

    • Document capture

      • Optical character recognition

• Information systems

  • Information systems applications