Handwritten Devnagari Digit Recognition using Fusion of Global and Local Features
Pratibha Singh, Ajay Kumar Verma, Narendra S. Chaudhari · International Journal of Computer Applications · 2014
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..