Abstract
Handwriting recognition is a very challenging probl em. 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 c haracter recognition. A successful handwritten Arab ic character recognition system improves interactivity between humans and computers. A successful handwritten Arabic character recognition system cannot be fulfi lled without using suitable feature extraction and classification methods. The main theme of this study is on-line ha ndwritten Arabic character recognition. The foremost contribution of this study is to propose a rule bas ed 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 fil ter were used to identify the features of the chara cters. The training and testing of the online handwriting reco gnition 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 cl assification phase. The results show that the propo sed 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 res ults proved that the proposed method can obtain a competitive r esult comparing with state of the art methods.
Highlights
Automatic character or text recognition of handwriting can be classified into two approaches
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
Summary
Automatic character or text recognition of handwriting can be classified into two approaches. The systems recognized text is originally written on papers. In such systems, the study sheets are digitized into two dimensional images. Features for recognition are first enhanced and extracted from the bitmap images by digital image processing. This type of recognition is called the offline recognition approach. In the online handwriting recognition approach, the user writes on a digital device using a special stylus, the system samples and records the point sequence as it is being written. The online handwriting samples contain additional temporal data, which is not present in offline sampled data (Mezghani et al, 2003; Bakhtiari-haftlang, 2007)
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