Javanese Script Recognition based on Metric, Eccentricity and Local Binary Pattern

Handwriting recognition is one of the most interesting researches in computer vision. Some previous research has developed and implemented Javanese script recognition in digital fonts and handwritten, but handwriting recognition is still not optimal. The contribution to this research is to improve the recognition accuracy in handwritten Javanese scripts. The proposed method is to combine metric feature extraction, eccentricity, and local binary pattern (LBP) which is further classified with k-Nearest Neighbor (KNN). Several preprocessing stages are carried out so that the features are extracted optimally. After testing the proposed method succeeded in improving recognition performance with 92.5% accuracy, 92.5% recall, and 100% precision on 200 training data and 40 testing data.