Abstract
In this paper, the statistical feature extraction techniques are explored, incrementally combined using different methods and analyzed for the recognition of isolated offline handwritten Devanagari numerals. The techniques selected are zoning, directional distance distribution, Zernike moments, discrete cosine transform, and Gabor filter that encapsulate the mutually exclusive statistical features like average pixel densities, directional distribution, orthogonal invariant moments, elementary frequency components, and space frequency component, respectively. The standard benchmark handwritten Devanagari numeral database provided by ISI, Kolkata, is used for the experimentation and 1-nearest neighbor and support vector machine for classification. The accuracy achieved with individual feature extraction techniques ranges from 86.87% to 98.96%. Further, features are integrated with methods like feature concatenation, majority voting, and a new proposed methodology by us named winners pooling. The maximum recognition obtained through feature integration is 99.14%.
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Acknowledgements
The authors would like to thank CVPR unit, ISI, Kolkata for providing us the handwritten database of Devnagari Numerals for experimentation purpose. In this work, the first author is funded under the UGC Fellowship given to college teachers for completion of Ph. D. and express gratitude for the same.
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Arya, S., Chhabra, I., Lehal, G.S. (2018). Integrated Feature Exploration for Handwritten Devanagari Numeral Recognition. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-10-7895-8_12
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