Offline handwritten Gurmukhi character recognition: study of different feature-classifier combinations

Abstract

Offline handwritten character recognition (OHCR) is the method of converting handwritten text into machine processable layout. Since late sixties, efforts have been made for offline handwritten character recognition throughout the world. Principal Component Analysis (PCA) has also been used for extracting representative features for character recognition. In order to assess the prominence of features in offline handwritten Gurmukhi character recognition, we have recognized offline handwritten Gurmukhi characters with different combinations of features and classifiers. The recognition system first sets up a skeleton of the character so that significant feature information about the character can be extracted. For the purpose of classification, we have used k-NN, Linear-SVM, Polynomial-SVM and RBF-SVM based approaches. In present work, we have collected 7,000 samples of isolated offline handwritten Gurmukhi characters from 200 different writers. The set of basic 35 akhars of Gurmukhi has been considered here. A partitioning policy for selecting the training and testing patterns has also been experimented in present work. We have used zoning feature; diagonal feature; directional feature; intersection and open end points feature; transition feature; parabola curve fitting based feature and power curve fitting based feature extraction technique in order to find the feature set for a given character. The proposed system achieves a recognition accuracy of 94.8% when PCA is not applied and a recognition accuracy of 97.7% when PCA is applied.