Online Arabic Handwritten Character Recognition Based on a Rule Based Approach
Saad M. Ismail and Siti Norul Huda Sheikh Abdullah
DOI : 10.3844/jcssp.2012.1859.1868
Journal of Computer Science
Volume 8, Issue 11
Handwriting recognition is a very challenging problem. Much work has been done on the recognition of Latin characters but limited work has been done on recognizing Arab characters. Most Arabic handwriting recognition in previous works focused on recognizing offline script and little take the online cases. The main theme of this study is on-line handwritten Arabic character recognition. A successful handwritten Arabic character recognition system improves interactivity between humans and computers. A successful handwritten Arabic character recognition system cannot be fulfilled without using suitable feature extraction and classification methods. The main theme of this study is on-line handwritten Arabic character recognition. The foremost contribution of this study is to propose a rule based production method to recognize Arabic characters based on the proposed hybrid Edge Direction Matrixes and geometrical feature extraction method. In addition, a horizontal and vertical projection profile and a Laplacian filter were used to identify the features of the characters. The training and testing of the online handwriting recognition system was conducted using our dataset; it has used 504 characters from different writers for training and 336 characters from different writers for testing. The evaluation was conducted on state of the art methods in the classification phase. The results show that the proposed method gives a competitive recognition rate for character categorywas 97.6%. The proposed approach succeeded in providing high recognition rate to match characters based on the shape and edges of character. The results proved that the proposed method can obtain a competitive result comparing with state of the art methods.
© 2012 Saad M. Ismail and Siti Norul Huda Sheikh Abdullah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.