원문정보
초록
영어
In this paper we present a comparison between two methods of learning-classification, the first is the K-Nearest Neighbors (KNN) and the second is the Support Vectors Machines (SVM), these both methods are supervised and used for the recognition of handwritten Latin numerals that are extracted from the MNIST standard database. The recognition process organized as follows: in the pre-processing of numeral images, we exploited the thresholding, the centering and the normalization techniques, in the features extraction we have used the morphology mathematical, the zoning and the zig-zag methods. The classification methods include the K-Nearest Neighbors and the Support Vectors Machines. Our experiments results proved the highest test accuracies 93.13% and 86.50% respectively with SVM and KNN classifiers. The simulation results that we obtained demonstrate the SVM is more performing than the KNN in this recognition.
목차
1. Introduction
2. Recognition System
3. Database
4. Pre-processing
5. Features Extraction
5.1. Extraction by Zoning Method
5.2. Extraction by Zig-zag Method
5.3. Extraction by Mathematical Morphology Method
5.4. Extraction by Hybrid Method: Zoning + Mathematical Morphology + Zig-zag
6. Learning-classification Phase
6.1. The K-nearest Neighbors (knn)
6.2. The Supports Vectors Machines
7. Test and Results
7.1. Mnist Numerals( MN) Recognition using SVM
7.2. Mnist Numerals (MN) Recognition using K-NN
8. Conclusion
Acknowledgements
References