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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