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Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 89 - Number 1 |
| Year of Publication: 2014 |
| Authors: Pratibha Singh, Ajay Verma, Narendra S. Chaudhari |
10.5120/15464-3628
|
Pratibha Singh, Ajay Verma, Narendra S. Chaudhari . Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features. International Journal of Computer Applications. 89, 1 ( March 2014), 6-12. DOI=10.5120/15464-3628
Abstract
We give our formulation for a ten class classification of handwritten Hindi digit recognition. Automatic Recognition of Handwritten Devnagri Numerals is a difficult task, because of the variability in writing style; pen used for writing and the color of handwriting, unlikely the printed character. Furthermore, Hindi Digit can be drawn in different sizes. Therefore, a robust offline Hindi handwritten recognition system has to account for all of these factors. Hence we have chosen a combination of global and local features. The global features are the structural features like endpoint, crosspoint, centroid of the loop, u shaped structure, C shaped structure and inverted C shaped structure. The local set of features combine the distance of thinned image from geometric centroid calculated zone-wise and histogram based features calculated zone-wise. Variability in writing style is taken care by size normalization and normalization to constant thickness as preprocessing a step before feature extraction. We used an Artificial Neural Network as classifier for recognition. Our method results in average correct rate of 95% or better. The combination of local and global features results in reduced confusion value. .
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