Abstract
This paper describes a system for isolated Kannada handwritten numerals recognition using image fusion method. Several digital images corresponding to each handwritten numeral are fused to generate patterns, which are stored in 8x8 matrices, irrespective of the size of images. The numerals to be recognized are matched using nearest neighbor classifier with each pattern and the best match pattern is considered as the recognized numeral.The experimental results show accuracy of 96.2% for 500 images, representing the portion of trained data, with the system being trained for 1000 images. The recognition result of 91% was obtained for 250 test numerals other than the trained images. Further to test the performance of the proposed scheme 4-fold cross validation has been carried out yielding an accuracy of 89%.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley, New York (1973)
Gader, P., Khabou, M.: Automated Feature Generation for Handwritten Digit Recognition. IEEE Transactions On Pattern Analysis And Machine Intelligence 18(12), 1256–1261 (1996)
Casy, R., Lecolinet, E.: A Survey Of Methods And Strategies In Character Segmentation. IEEE Transactions On Pattern Analysis And Machine Intelligence 18(7), 690–706 (1996)
Plamodin, R., Srihari, S.N.: Online and Offline Handwriting Recognition: A Comprehensive survey. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(1), 63–84 (2000)
Trier, O., Jain, A.K., Taxt, T.: Feature Extraction Methods For Character Recognition - A Survey Pattern Recognition. 29(4), 641–662 (1996)
Nagabhushan, P., Angadi, S.A., Anami, B.S.: An Intelligent Pin Code Script Identification Methodology Based On Texture Analysis using Modified Invariant Moments. In: Proc. of ICCR, pp. 615–623. Allied Pub (2005)
Hanmandlu, M., Mohan, R.M., Chakraborty, S., Goyal, S., Choudhary, D.R.: Unconstrained Handwritten Character Recognition Based on Fuzzy Logic. Pattern Recognition 36, 603–623 (2003)
Kang, H.J.: Combining Multiple Classifiers Based on Third Order Dependency For Handwritten Numeral Recognition. Pattern Recognition Letters 24, 3027–3036 (2003)
Aradhya, V.N.M., Kumar, G.H., Noushath, S.: Robust Unconstrained Handwritten Digit Recognition Using Radon Transform. In: Proc. of IEEE-ICSCN 2007, IEEE Computer Society Press, Los Alamitos (2007)
Ashvin, T.V., Shastry, P.S.: A Font And Size Independent Ocr System For Printed Kannada Documents using Support Vector Machines. Sadhana 27(1), 23–24 (2002)
Sharma, N., Pal, U., Kimura, F.: Recognition of Handwritten Kannada Numerals. In: Proc. of 9th International Conference on Information Technology (ICIT 2006), pp. 133–136 (2006)
Hariharana, S.: Recognition of Handwritten Numerals Through Genetic Based Machine Learning. Jour. of the CSI 30(2), 16–22 (2000)
Bhattacharya, U., et al.: Recognition of Hand Printed Bangla Numerals Using Neural Network Models. In: Pal, N.R., Sugeno, M. (eds.) AFSS 2002. LNCS (LNAI), vol. 2275, pp. 228–235. Springer, Heidelberg (2002)
Raju, G.: Recognition Of Unconstrained Handwritten Malayalam Characters Using Zero Crossing of Wavelet Coefficients. In: Proc. of ADCOM, pp. 217–221 (2006)
Dinesh Acharya, U., Subbareddy, N.V., Krishnamoorthy,: Isolated Kannada Numeral Recognition Using Structural Features and K-Means Cluster. In: Proc. of IISN-2007, pp. 125–129 (2007)
Chakraborty, R., Sil, J.: Handwritten Character Recognition Systems Using Image Fusion And Fuzzy Logic. In: PReMI 2005. LNCS, vol. 3766, pp. 344–349. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rajput, G.G., Hangarge, M. (2007). Recognition of Isolated Handwritten Kannada Numerals Based on Image Fusion Method. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-77046-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77045-9
Online ISBN: 978-3-540-77046-6
eBook Packages: Computer ScienceComputer Science (R0)