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https://ptsldigital.ukm.my/jspui/handle/123456789/463828Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Siti Nurul Huda Sheikh Abdullah, Dr. | - |
| dc.contributor.author | Saad Mohammad Saad Ismail (P43544) | - |
| dc.date.accessioned | 2023-09-25T09:40:16Z | - |
| dc.date.available | 2023-09-25T09:40:16Z | - |
| dc.date.issued | 2011-09-05 | - |
| dc.identifier.other | ukmvital:114849 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/463828 | - |
| dc.description | Handwriting recognition is one of the very challenging problems. Much work has been done on the recognition of Latin characters but limited work done on recognizing Arab characters. Most of Arabic handwriting recognition in previous works focused on recognizing offline script and little take the online cases. The main theme of this research is the on-line handwritten Arabic character recognition. A successful handwritten Arabic character recognition system improves interactivity between human and computers. Several problems faced recognition process; feature extraction is the most important problem in character recognition. A successful handwritten Arabic character recognition system cannot be fulfilled without used a suitable feature extraction and classification methods. The main theme of this research is the on-line handwritten Arabic character recognition. And the foremost contribution of this research is to propose a rule based production method to recognize Arabic character based on the proposed hybrid Edge Direction Matrixes and geometrical feature extraction method. In addition, horizontal and vertical projection profile, and Laplacian filter were used to identify the feature of character. The training and testing of the online handwriting recognition system was conducted using our dataset; it has been used 504 characters from different writers for training and 336 characters from different writers for testing. The evaluation were conducted on state of the art methods in both feature extraction and classification phase. The results show that the proposed method gives better recognition rate for character category.,'Certification of Master's/Doctoral Thesis' is not available,Master Information Technology | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Science and Technology / Fakulti Sains dan Teknologi | - |
| dc.rights | UKM | - |
| dc.subject | Optical character recognition | - |
| dc.subject | Arabic character sets (Data processing) | - |
| dc.subject | Pattern recognition systems | - |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | - |
| dc.subject | Dissertations, Academic -- Malaysia | - |
| dc.title | Online arabic handwritten character recognition based on multi features and rule based production | - |
| dc.type | theses | - |
| dc.format.pages | 86 | - |
| dc.identifier.callno | TA1640.I845 2011 tesis | - |
| dc.identifier.barcode | 002456(2011) | - |
| Appears in Collections: | Faculty of Science and Technology / Fakulti Sains dan Teknologi | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_114849+SOURCE1+SOURCE1.0.PDF Restricted Access | 12.38 MB | Adobe PDF |  View/Open |
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