원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.7 No.6
2014.11
pp.295-306
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this research, we present two comparative studies; the first one is between two methods of features extraction which are the mathematical morphology, the zoning and the hybridization of these two methods. The second comparative study is between both supervised methods used in learning-classification which are the Multi-Layer Perceptron (MLP) and the Support Vector Machines (SVM) applied to cursive handwritten Tifinagh characters recognition. The obtained experimental result demonstrates that the hybrid method is most efficient and the SVM is more performing than the MLP.
목차
Abstract
1. Introduction
2. Recognition System
2.1. Tifinagh Character Database
2.2. Pre-processing
2.3. Features Extraction
3. Learning-classification Phase
3.1. The Neural Networks (NNs)
3.2. The Supports Vectors Machines
4. Experiments and Results
4.1. Recognition using MLP
4.2. Recognition Using the SVM
5. Conclusion
Acknowledgements
References
1. Introduction
2. Recognition System
2.1. Tifinagh Character Database
2.2. Pre-processing
2.3. Features Extraction
3. Learning-classification Phase
3.1. The Neural Networks (NNs)
3.2. The Supports Vectors Machines
4. Experiments and Results
4.1. Recognition using MLP
4.2. Recognition Using the SVM
5. Conclusion
Acknowledgements
References
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저자정보
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자료제공 : 네이버학술정보