Multifont Ottoman character recognition
| dc.citation.epage | 949 | en_US |
| dc.citation.spage | 945 | en_US |
| dc.contributor.author | Öztürk, Ali | en_US |
| dc.contributor.author | Güneş, S. | en_US |
| dc.contributor.author | Özbay, Y. | en_US |
| dc.coverage.spatial | Jounieh, Lebanon, Lebanon | en_US |
| dc.date.accessioned | 2016-02-08T11:58:32Z | |
| dc.date.available | 2016-02-08T11:58:32Z | |
| dc.date.issued | 2000 | en_US |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Date of Conference: 17-20 Dec. 2000 | en_US |
| dc.description.abstract | Ottoman characters from three different fonts are used character recognition problem, broadly speaking, is transferring a page that contain symbols to the computer and matching these symbols with previously known or recognized symbols after extraction the features of these symbols via appropriate preprocessing methods. Because of silent features of the characters, implementing an Ottoman character recognition system is a difficult work. Different researchers have done lots of works for years to develop systems that would recognize Latin characters. Although almost one million people use Ottoman characters, great deal of whom has different native languages, the number of studies on this field is insufficient. In this study 28 different machine-printed to train the Artificial Neural Network and a %95 classification accuracy for the characters in these fonts and a %70 classification accuracy for a different font has been found. | en_US |
| dc.identifier.doi | 10.1109/ICECS.2000.913032 | en_US |
| dc.identifier.isbn | 0780365429 | |
| dc.identifier.uri | http://hdl.handle.net/11693/27639 | |
| dc.language.iso | English | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isversionof | https://doi.org/10.1109/ICECS.2000.913032 | en_US |
| dc.source.title | ICECS 2000. 7th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.00EX445) | en_US |
| dc.subject | Artificial neural network | en_US |
| dc.subject | Character recognition system | en_US |
| dc.subject | Classification accuracy | en_US |
| dc.subject | Native language | en_US |
| dc.subject | Pre-processing method | en_US |
| dc.subject | Character recognition | en_US |
| dc.subject | Feature extraction | en_US |
| dc.subject | Neural networks | en_US |
| dc.title | Multifont Ottoman character recognition | en_US |
| dc.type | Conference Paper | en_US |
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