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Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts

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Contemporary Computing (IC3 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 40))

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Abstract

India is a multi-lingual multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose a zone based hybrid feature extraction algorithm scheme towards the recognition of off-line handwritten numerals of south Indian scripts. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance and Average angle from the character centroid to the pixels present in the zone are computed (two features). Similarly zone centroid is computed (two features). This procedure is repeated sequentially for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 4*n such features are extracted. Nearest neighbor classifier is used for subsequent classification and recognition purpose. We obtained 97.55 %, 94 %, 92.5% and 95.2 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

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References

  1. Pal, U., Chaudhuri, B.B.: Indian Script Character recognition: A survey. Pattern Recognition 37, 1887–1899 (2004)

    Article  Google Scholar 

  2. Anil, K.J., Taxt, T.: Feature Extraction Methods for Character Recognition-A Survey. Pattern Recognition 29(4), 641–662 (1996)

    Article  Google Scholar 

  3. Majumdar, A., Chaudhuri, B.B.: Printed and handwritten Bangla numeral recognition using multiple classifier outputs. In: Proceedings of the first IEEE ICSIP 2006, vol. 1, pp. 190–195 (2006)

    Google Scholar 

  4. Abdur, R., Shuvabranta, S., Rahman, M., Sattar, A.: Recognition of conjunctive Bangla characters by artificial neural network. In: International Conference on Information and Communication Technology, pp. 96–99 (2007)

    Google Scholar 

  5. Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: Off line handwritten character recognition of Devanagiri Scripts. In: Ninth International Conference on Document Analysis as Recognition (ICDAR 2007), pp. 496–500 (2007)

    Google Scholar 

  6. Patil, P.M., Sontakke, T.R.: Rotation scale and translation invariant handwritten Devanagiri numeral character recognition using fuzzy neural network. Elsevier, Pattern Recognition 40, 2110–2117 (2007)

    Article  Google Scholar 

  7. Pal, U., Sharma, N., Wakabayashi, T., Kimura, F.: A system for off-line Oriya handwritten character recognition using curvature feature. In: 10th International Conference on Information Technology, pp. 227–229. IEEE, Los Alamitos (2007)

    Google Scholar 

  8. Hanmandlu, M., Grover, J., Madasu, V.: Input fuzzy for the recognition of handwritten Hindi numeral. In: International Conference on Informational Technology, vol. 2, pp. 208–213 (2007)

    Google Scholar 

  9. Pujari, A.K., Dhanunjaya Naidu, C., Sreenivasa Rao, M., Jinaga, B.C.: An Intelligent character recognizer for Telugu scripts using multi resolution analysis and associative memory. Elsevier, Image vision Computing, 1221–1227 (2004)

    Google Scholar 

  10. Suresh, R.M., Arumugam, S.: Fuzzy technique based recognition of handwritten characters. Elsevier, Image Vision Computing 25, 230–239 (2007)

    Article  Google Scholar 

  11. Lajish, V.L.: Handwritten character using gray- scale based state-space parameters and class modular neural network. In: IEEE International Conference on signal processing, Comunication and Networking, pp. 374–379 (2008)

    Google Scholar 

  12. Pal, U., Wakabayashi, T., Kimura, F.: Handwritten numeral recognition of six popular scripts. In: Ninth International conference on Document Analysis and Recognition ICDAR 2007, vol. 2, pp. 749–753 (2007)

    Google Scholar 

  13. Rajput, G.G., Hangarge, M.: Recognition of isolated handwritten Kannada numerals based on image fusion method. In: Ghosh, A., De, R.K., Pal, S.K. (eds.) PReMI 2007. LNCS, vol. 4815, pp. 153–160. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Rajashekararadhya, S.V., Vanaja, R.: Isolated handwritten Kannada digit recognition: A novel approach. In: Proceedings of the International Conference on Cognition and Recognition, pp. 134–140 (2008)

    Google Scholar 

  15. Rajashekararadhya, S.V., Vanaja, R., Manjunath Aradhya, V.N.: Isolated handwritten Kannada and Tamil numeral recognition: A novel approach. In: First International Conference on Emerging Trends in Engineering and Technology ICETET 2008, pp. 1192–1195 (2008)

    Google Scholar 

  16. Rajashekararadhya, S.V., Vanaja, R.: Handwritten numeral recognition of three popular South Indian scripts: A novel approach. In: Proceedings of the second International Conference on information processing ICIP, pp. 162–167 (2008)

    Google Scholar 

  17. Rajashekararadhya, S.V., Vanaja, R.: Neural network based handwritten numeral recognition of Kannada and Telugu scripts. In: TENCON 2008, Hyderabad, pp. 1–5 (2008)

    Google Scholar 

  18. Gonzalez, R.C., Woods, R.E., Steven, L. (Eddins): Digital Image. In: Processing using MATLAB, Pearson Education, Dorling Kindersley, South Asia (2004)

    Google Scholar 

  19. Rajashekararadhya, S.V., Vanaja, R.: A novel zone based feature extraction algorithm for handwritten numeral recognition of four Indian scripts. Digital Technology Journal 2009 2, 41–51 (2009)

    Google Scholar 

  20. Bhattacharya, U., Chaudhuri, B.B.: Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals. IEEE Transaction on Pattern analysis and machine intelligence 31(3), 444–457 (2009)

    Article  Google Scholar 

  21. Bhattacharya, U., Chaudhuri, B.B.: Databases for research on recognition of handwritten characters of Indian scripts. In: Proceedings of the 8th International conference on document analysis and recognition ICDAR 2005, Seoul, Korea, vol. II, pp. 789–793 (2005)

    Google Scholar 

  22. Chaudhuri, B.B.: A complete handwritten numeral database of Bangla – a major Indic script. In: Proceedings of the 10th International workshop on frontiers of handwriting recognition La Baule, France, pp. 379–784 (2006)

    Google Scholar 

  23. Bhattacharya, U., Chaudhuri, B.B.: A majority voting scheme for multiresolution recognition of hand-printed numerals. In: Proceedings of the 7th International conference on document analysis and recognition ICDAR 2003, Edinburgh, Scotland, pp. 789–793 (2005)

    Google Scholar 

  24. Wen, Y., Lu, Y., Shi, P.: Handwritten Bangla numeral recognition system and its application to postal automation. Pattern recognition 40(1), 99–107 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Research Scholar, India

    S. V. Rajashekararadhya

  2. Asst.professor Department of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai, 600 025, India

    P. Vanaja Ranjan

Authors
  1. S. V. Rajashekararadhya
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  2. P. Vanaja Ranjan
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Editor information

Editors and Affiliations

  1. Dept. of Computer Sciences, University of Florida, Gainesville, FL, 32611, USA

    Sanjay Ranka

  2. Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, USA

    Srinivas Aluru

  3. Grid Computing and Distributed Systems Laboratory and, NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia

    Rajkumar Buyya

  4. Department of Computer Science, National Tsing Hua University, Taiwan

    Yeh-Ching Chung

  5. Computer Science, College of Engineering and Science, Louisiana Tech University, Ruston, LA, 71272, USA

    Sumeet Dua  & Vir V. Phoha  & 

  6. Department of Computer Sciences, Purdue University, W. Lafayette, IN, 47907, USA

    Ananth Grama

  7. Arizona State University, Tempe, AZ, 85281, USA

    Sandeep K. S. Gupta

  8. Computer Science and Engineering Department, Indian Institute of Technology Kharagpur, 721 302, WB, India

    Rajeev Kumar

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© 2009 Springer-Verlag Berlin Heidelberg

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Rajashekararadhya, S.V., Ranjan, P.V. (2009). Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts. In: Ranka, S., et al. Contemporary Computing. IC3 2009. Communications in Computer and Information Science, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03547-0_14

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  • DOI: https://doi.org/10.1007/978-3-642-03547-0_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03546-3

  • Online ISBN: 978-3-642-03547-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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