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| Content Provider | IEEE Xplore Digital Library | 
|---|---|
| Author | Win, H.P.P. Khine, P.T.T. Tun, K.N.N. | 
| Copyright Year | 2011 | 
| Description | Author affiliation: University of Computer Studies, Yangon, Myanmar (Win, H.P.P.; Khine, P.T.T.; Tun, K.N.N.) | 
| Abstract | The large amount of Myanmar document images are getting archived by the Digital Libraries, an efficient strategy is needed to convert document image into machine understandable text format. The state of the art OCR systems can't do for Myanmar scripts as our language pose many challenges for document understanding. Therefore, this paper plans an OCR system for Myanmar Printed Document (OCRMPD) with several proposed methods that can automatically convert Myanmar printed text to machine understandable text. Firstly, the input image is enhanced by making some correction on noise variants. Then, the characters are segmented with a novel segmentation method. The features of the isolated characters are extracted with a hybrid feature extraction method to overcome the similarity problems of the Myanmar scripts. Finally, hierarchical mechanism is used for SVM classifier for recognition of the character image. The experiments are carried out on a variety of Myanmar printed documents and results show the efficiency of the proposed algorithms. | 
| Starting Page | 96 | 
| Ending Page | 101 | 
| File Size | 826657 | 
| Page Count | 6 | 
| File Format | |
| ISBN | 9781457715389 | 
| e-ISBN | 9781457715396 | 
| DOI | 10.1109/ICDIM.2011.6093371 | 
| Language | English | 
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Publisher Date | 2011-09-26 | 
| Publisher Place | Australia | 
| Access Restriction | Subscribed | 
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) | 
| Subject Keyword | Support vector machines Image segmentation Accuracy Text recognition support vector machines feature extraction OCRMPD Myanmar scripts Feature extraction segmentation Optical character recognition software Character recognition | 
| Content Type | Text | 
| Resource Type | Article | 
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