Online Handwritten Telugu Stroke Recognition using Direction Operator for Feature Extraction

Authors

  • Srilakshmi Inuganti Research Scholar, Computer Science and Engineering, Jawaharlal Nehru technological university Author
  • Dr. R. Rajeshwara Rao Professor, Computer Science and Engineering, Jawaharlal Nehru technological university, University college of engineering, vijayanagaram, Author

DOI:

https://doi.org/10.61841/gtp8s419

Keywords:

Direction operator, Hybrid Approach, Stroke Recognition, Online

Abstract

Important phase of Character Recognition is Feature extraction. In this paper a feature extraction technique using 8-point interconnection method named as Direction operator is proposed. The feature obtained by using direction operator represents structural, global and local characteristics of a stroke. This feature represents (x,y) coordinate of each point on the stroke with 8-bit vector, where each vector gives interconnection of this point with other points in the stroke in 8-directions.A two stage classifier known as Hybrid classifier is also proposed, in which direction operator feature is used in preclassifier and preprocessed(x,y) coordinates are used in postclassifier. We have verified this feature with HP-Lab data available in UNIPEN format. Experimental results proved that direction operator feature improves recognition accuracy over the chosen dataset.

 

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References

1. Srilakshmi .I,RajeshwaraRao.R, “Survey on Online Handwritten Indian Character Recognition and its Extension to Telugu Character Recognition”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6S5, April 2019

2. Srilakshmi.I,RajeshwaraRao.R,”Preprocessing of online handwritten Telugu character recognition”International Journal of Advanced and Applied Sciences, 4(7) 2017, Pages: 179-189

3. D. Das, R. Devi, S. Prasanna, S. Ghosh and K. Naik "Performance comparison of online handwriting recognition system for assamese language based on hmm and svmmodelling", International Journal of Computer Science & Information Technology, vol. 6, no. 5, 2014 .

4. Mary, a. Anci manon, m. Bhuvaneswari, n. Haritha, v. Krishnaveni, and b. Punithavathisivathanu. "design of automatic number plate recognition system for moving vehicle." international journal of communication and computer technologies 7 (2019), 1-5. Doi:10.31838/ijccts/07.sp01.01

5. Bahlmann, C., 2006. Directional feature in online handwriting recognition. Pattern Recognition 39(1), 115–125.

6. N. Kato, M. Suzuki, S. Omachi, H. Aso, and Y. Nemoto.A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance. IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(3):258–262, March 1999.

7. Mausam j. Naik (2019) mapk signalling pathway: role in cancer pathogenesis. Journal of Critical Reviews, 6 (3), 1-6. doi:10.22159/jcr.2019v6i3.31778

8. Kherallah, M., Haddad, L., Alimi, A., Mitiche, A., 2008. On-line handwrit ten digit recognition based on trajectory and velocity modeling. Pattern Recognition Letters 29, 580–594.

9. Mori, M., Uchida, S., Sakano, H.: Global feature for online character recognition. Pattern Recognit.Lett. 35, 142–148 (2014)

10. W. Zeng, X. Meng, C. Yang, L. Huang, Feature extraction for online handwritten characters using Delaunay triangulation, Comput. Graph.UK 30 (2006).

11. Mori, M., Wakahara, T., Ogura, K., 1998. Measures for structural and global 334 shape description in handwritten kanji character recognition. In: Document Recognition V. Vol. 3305. pp. 81–89

12. K. H. Aparna, Vidhya Subramanian, M. Kasirajan, G. Vijay Prakash, V. S. Chakravarthy, SriganeshMadhvanath, ”Online handwriting recognition for Tamil”, Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR’ 04), Tokyo, Japan, 2004, pp 438-443.

13. SubhasisMandal,S.R,SureshSundaram,” An improved discriminative region selection methodology for online handwriting recognition”,InInternational Journal of Document Analysis and Recognition ,Volume 22 Issue 1,March 2019,Pages 1-14

14. Pathirage Kamal Perera. "Traditional medicine-based therapies for cancer management." Systematic Reviews in Pharmacy 10.1 (2019), 90-92. Print. doi:10.5530/srp.2019.1.15

15. Singh, S., Sharma, A., Chhabra, I.: A dominant points-based feature extraction approach to recognize online handwritten strokes. Int. J. Doc. Anal. Recognit. (IJDAR),2007, 20(1), 37–58

16. Mandalapu, D., Krishna, S. M., 2007. A feature based on encoding the relative position of a point in the character for online handwritten character recognition. In: ICDAR’07. Vol. 2. pp. 1014–1017

17. Srilakshmi .I,RajeshwaraRao.R, “Preprocessing of online handwritten Telugu character recognition”,International Journal of Control Theory and Applications 8 (5), 1939-45,2015

18. Zhang, W., Liu, H., Al-Shabrawey, M., Caldwell, R., Caldwell, R.Inflammation and diabetic retinal microvascular complications(2011) Journal of Cardiovascular Disease Research, 2 (2), pp. 96-103.

DOI: 10.4103/0975-3583.83035

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Published

31.10.2020

How to Cite

Inuganti, S., & Rao, R. R. (2020). Online Handwritten Telugu Stroke Recognition using Direction Operator for Feature Extraction. International Journal of Psychosocial Rehabilitation, 24(8), 530-537. https://doi.org/10.61841/gtp8s419