Development of an Arabic Handwriting Learning Educational System

In today many students produce a wrong and illegible handwriting and teachers need a lot of time to check the handwriting errors. The traditional approach for handwriting teaching needs a long hours of handwriting practice. Unfortunately, this is not advantageous in many cases. In this paper, we develop an automated educational system for Arabic handwriting learning and detection of errors such as: the stroke production errors, stroke sequence errors, stroke relationship errors and stroke interline errors. Then our system can check the handwriting mistakes and immediately provide a feedback to the students to correct themselves. In this work, we used an attributed relational graph to locate the handwriting mistakes. The experimental results show that our proposed system can treat multiple cases of errors with satisfied accuracy.