Recognition of On-line Arabic Handwritten Characters Using Structural Features

In this study, an efficient approach for the recognition of on-line Arabic handwritten characters is presented. The approach is based on structural features and decision tree learning techniques. The proposed approach consists of three phases: First, the user writes the character on a special window on the screen, and then the coordinates of the pixels forming the character is captured and stored in a special array. Second, a bounding box of 5x5 is drawn around the character, and five features are extracted from the character that used in step three for the recognition of the character through the use of a decision tree learning techniques. The proposed approach is tested on a set of 1400 different characters written by ten users. Each user wrote the 28 Arabic characters five times in order to get different writing variations. Experiment results showed the effectiveness of the novel approach for recognizing handwritten Arabic characters.