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| dc.contributor.author | Uzma Naqvi, 01-244111-029 | |
| dc.date.accessioned | 2017-08-02T05:15:03Z | |
| dc.date.available | 2017-08-02T05:15:03Z | |
| dc.date.issued | 2013 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/3420 | |
| dc.description | Supervised by Dr. Shehzad Khalid | en_US |
| dc.description.abstract | Introduction state of the art feature extraction proposed framework Experimentle studies Conclusion | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Software Engineering, Bahria University Engineering School Islamabad | en_US |
| dc.relation.ispartofseries | MS SE;T-0677 | |
| dc.subject | Software Engineering | en_US |
| dc.title | Framework for human identification through offline handwritten documents (T-0677) (MFN 3844) | en_US |
| dc.type | MS Thesis | en_US |
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MS(SE) (BUES) [194]