Accuracy Improvement of Devnagari Character Recognition Combining SVM and MQDF

This paper deals with the recognition of off-line handwritten Devnagari characters. Here two sets of feature are computed and two classifiers are combined to get higher accuracy of Devnagari character recognition. Dimension of the features vector of each set is 392. First feature set is computed based on the directional information obtained from the arc tangent of the gradient. Since most of the Devnagari handwritten characters have some curve-like parts, curvature-based feature guided by gradient information is computed for the second set of features. Combined use of Support Vector Machines (SVM) and Modified Quadratic Discriminant Function (MQDF) are applied here for better performance of Devnagari character recognition.