SVM Based Offline Handwritten Gurmukhi Character Recognition

Support Vector Machines (SVMs) have successfully been used in recognizing printed characters. In the present work, we have used this classification technique to recognize handwritten characters. Recognition of handwritten characters is a difficult task owing to various writing styles of individuals. A scheme for offline handwritten Gurmukhi character recognition based on SVMs is presented in this paper. The system first prepares a skeleton of the character, so that feature information about the character is extracted. Features of a character have been computed based on statistical measures of distribution of points on the bitmap image of character. SVM based approach has been used to classify a character based on the extracted features. In this work, we have taken the samples of offline handwritten Gurmukhi characters from one hundred different writers. The partition strategy for selecting the training and testing patterns has also been experimented in this work. We have used in all 3500 images of Gurmukhi characters for the purpose of training and testing. We have used diagonal and; intersection and open end points feature extraction techniques in order to find the feature sets for a given character. The proposed system achieves a maximum recognition accuracy of 94.29% with 90% training data and 10% testing data using intersection and open end points as features and SVM with polynomial kernel.