Offline handwritten Gurmukhi character recognition: k-NN vs. SVM classifier

In this paper, we analyzed the impact of combination of feature extraction and classification techniques for recognition of offline handwritten segmented Gurmukhi characters. Also principal component analysis (PCA) has been used to find efficient features from peak extent based and modified division point (MDP) based features which have further been used in the classification process. For classification, k-NN and SVM whose three different kernels, namely, linear-SVM, polynomial-SVM and RBF-SVM have been considered for recognition accuracy in this paper. The experimentation has been performed on the dataset of 8960 samples of offline handwritten Gurmukhi characters written by 160 unique writers. For selecting the training and testing dataset, five different partitioning strategies and k-fold cross validation techniques have been used. A recognition accuracy of 92.3% has been achieved, using the combination of linear-SVM, polynomial-SVM and k-NN classifiers and with the partitioning strategy of 80% data as training dataset and remaining data as testing dataset. © 2019, Bharati Vidyapeeth's Institute of Computer Applications and Management.