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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | El Abed, H. Margner, V. |
| Copyright Year | 2009 |
| Abstract | The recognition of handwritten characters, words, and text arouses great interest today. To develop the best working system is subject of many papers published. With this paper, methods to improve the performance of existing word recognition systems are discussed. The availability of a sufficient data sets for training and testing the system assumed, optimization algorithms are presented. The usage of different feature sets and the combination of different recognizers are proposed. Tests with Arabic handwriting recognition systems using the reference IfN/ENIT-database show the usefulness of the proposed methods. An improvement of the recognition rate of up to 28% of the best single system is achieved. |
| Starting Page | 1181 |
| Ending Page | 1185 |
| File Size | 223967 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424445004 |
| ISSN | 15205363 |
| DOI | 10.1109/ICDAR.2009.11 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-26 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Handwriting recognition Character recognition Feature extraction Hidden Markov models Text recognition System testing Text analysis Communications technology Availability Noise reduction IfN/ENIT Database Arabic Text Recognition Systems Combination |
| Content Type | Text |
| Resource Type | Article |
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