Performance Evaluation of Classifiers Applying Directional Features for Devnagri Numeral Recognition


Article Preview

Handwriting recognition is a special category of pattern recognition which is matured enough for English language, but for Hindi it is in development state. Among various features directional features found to outperform than the others. So in this paper, we have evaluated the performance of various direction features and various classifiers for the handwritten Devnagri numeral recognition. The character image is preprocessed and portioned into sub-images. The standard zoning is compared against flexible zoning. An experimental comparison of gradient features and chain code histogram feature is evaluated with Bays classifier, K-nn, fuzzy k-nn. For comparison of the performance, the error rate and complexity of computation and time is used as the measure. Gradient features are found to outperform among various directional features.



Advanced Materials Research (Volumes 403-408)

Edited by:

Li Yuan




P. Singh et al., "Performance Evaluation of Classifiers Applying Directional Features for Devnagri Numeral Recognition", Advanced Materials Research, Vols. 403-408, pp. 1042-1048, 2012

Online since:

November 2011




[1] Q. Due Trier, A.K. Jain, T. Taxt, Feature Extraction Methods for Character Recognition: A Survey, Pattern Recognition, 1996, 29(4), pp.641-662.


[2] C. -L. Liu, K. Nakashima, H. Sako, H. Fujisawa, Handwritten digit recognition: benchmarking of state-of the-art techniques, Pattern Recognition, 36(10): 2271-2285, (2003).


[3] Daugman. J. G. Two Dimensional Spectral Analysis of Cortical Receptive Field Profiles [J]. Vision Research, 1980, 20: 847-856.


[4] C. -L. Liu, K. Nakashima, H. Sako, H. Fujisawa, Handwritten digit recognition: investigation of normalization and feature extraction techniques, Pattern Recognition, 37(2): 265-279, (2004).


[5] A. Kawamura, K. Yura, T. Hayama, Y. Hidai, T. Minamikawa, A. Tanaka, and S. Masuda, On-Line Recognition of Freely Handwritten Japanese Characters Using Directional Feature Densities, " Proc. 11th Int, l Conf. Pattern Recognition, vol. 2, pp.183-186, (1992).


[6] I.K. Sethi and B. Chatterjee, Machine Recognition of constrained Hand printed Devnagari, Pattern Recognition, Vol. 9, pp.69-75, (1977).


[7] M. Hanmandlu and O.V. Ramana Murthy, Fuzzy Model Based Recognition of Handwritten Hindi Numerals, Intl. Conf. on Cognition Recognition, pp.490-496, (2005).


[8] R.J. Ramteke, P.D. Borkar and S.C. Mehrotra, Recognition of Marathi Handwritten Numerals: An Invariant Moments Approach, Intl. Conf. on Cognition Recognition, pp.482-489, (2005).

[9] U. Bhattacharya, B. B. Chaudhuri, R. Ghosh and M. Ghosh, On Recognition of Handwritten Devnagari Numerals, In Proc. of the Workshop on Learning Algorithms for Pattern Recognition (in conjunction with the 18th Australian Joint Conference on Artificial Intelligence), Sydney, pp.1-7, (2005).

[10] N. Sharma, U. Pal, F. Kimura and S. Pal Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier, ICVGIP 2006, LNCS 4338, p.805 – 816, Springer-Verlag Berlin Heidelberg (2006).


[11] S.V. Rajashekararadhya and P.V. Ranjan, Efficient Zone based feature extraction method for handwritten numeral recognition of four popular south Indian scripts, JATIT 2005-(2008).


[12] Pratibha Singh, Ajay Verma and N. S. Chaudhary, Classification of Hindi numeral using Fuzzy Zoning and SVM", Advanced computer and communication conference 2011.

Fetching data from Crossref.
This may take some time to load.