Abstract
Individuality of handwriting inserts varying curvatures and angles whenever someone writes a sample of a particular numeral which makes the task of its off-line recognition more challenging. The paper addresses both these issues in novel and robust ways by merging two Digital domains, namely Digital Communications and Digital Image Processing. Curvature is treated by finding analytical features based on distance and slope. Distance based treatment is done by means of Delta Distance Coding whereas slope based analysis is executed with Delta Slope Coding. Angular variations have been countered with the help of rotation invariant physical feature i.e., Pixel Moment of Inertia. A due stress has been laid on Pixel Moment of Inertia by finding it both globally and locally in terms of Centroidal Moment of Inertia and Zonal Moment of Inertia respectively. The above mentioned features are further supported with statistical features in order to differentiate very similar looking numeral pairs like (3, 8), (1, 7), (7, 9). Feature extraction methods are devoid of cumbersome calculations, and classifiers are capable of yielding instantaneous results. Therefore, the current system is a real time system. The system has been tested on unconstrained MNIST dataset. The overall recognition accuracy of 99.26% has been obtained.
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