HOG and Zone Base Features for Handwritten Javanese Character Classification

Abstract

The recognition of handwritten characters remain one of challenging task in character recognition problems. The variations created by each person in writing the characters affect the character recognition result. Many studies have been performed to increase the performance of Javanese character recognition. The efforts are to extract the best feature for classification or to get the best classifier for classification. In this study, HOG feature and Zoning Based Feature will be used to classify Javanese Characters. The performance of both features will be compared for classifying Javanese character by using SVM classifier. Two types of inputs will be used for each feature extractor, binary and skeleton of the character image. The experiment showed that HOG feature is able to show higher accuracy as compared to the simple zone based feature (88.45%). The best accuracy for HOG is achieved by using binary input. On the other hand, despite its simplicity zone based feature is able to achive 81.98% accuracy by using skeleton input. Considering that the zone based feature used in this research is simply the pixel count in each zone, future research may be performed to extract more statistical properties on each zone. Future works may also focus on rotation free feature extraction for Javanese character classification.