Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
