Performance analysis of different classifiers for recognition of handwritten Gurmukhi characters using hybrid features

The paper is focuses on using hybridization of multiple features with different classifiers for the purpose of recognition of isolated handwritten Gurmukhi character images. We have tested four different types of features named as Histogram Oriented Gradient (HOG), Distance Profile, Background Directional Distribution (BDD) and Zonal Based Diagonal (ZBD). HOG feature is computed by information of Directions provided from gradient´s tangent of arc. Distance Profile can be computed by counting pixels from bounding line of image of character to edge of character from different directions. BDD feature can be computed by background distribution of foreground pixels to background pixels in eight different directions. For computation of ZBD feature, image is segmented into 100 equal zones then feature is calculated from pixels of each zone by traveling along its diagonal direction. For these experiment seven thousand isolated images of Gurmukhi characters have been tested. The experiment achieves a maximum recognition accuracy of 97.257% with 5-fold and 97.671% with 10-fold cross validation by applying hybrid features on SVM classifier.