원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.4 No.3
2011.07
pp.51-60
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper proposes a new classification method for Farsi handwritten word recognition using gradient and gradient based features. The extracted feature vectors were classified using two Multi Layer Perceptron networks as basic experts, and one Radial Basis Function was applied to choose the best expert. The experiments were performed using the Iranshahr dataset. This dataset consists of 780 samples of 30 city names of Iran out of which, 600 samples were used to train the network and 180 samples to test it. A set of experiments were conducted to compare proposed method with some other combination rules. Results show that the proposed method achieved 91.11% recognition rate.
목차
Abstract
1. Introduction
2. Correlation Reduction Strategies in Multiple Classifier Systems
2.1. Different representation of patterns
2.2 Different Learning Machines
2.3 Different Labeling in Learning
3. Feature Extraction
3.1. Gradient
3.2. Gradient Based Feature Extraction Method
3.3. Principal Component Analysis (PCA)
3.4. Linear Discriminate Analysis (LDA)
4. Proposed New Combination Method Based on Different Representation of Data
5. Experimental Results
6. Conclusion
References
1. Introduction
2. Correlation Reduction Strategies in Multiple Classifier Systems
2.1. Different representation of patterns
2.2 Different Learning Machines
2.3 Different Labeling in Learning
3. Feature Extraction
3.1. Gradient
3.2. Gradient Based Feature Extraction Method
3.3. Principal Component Analysis (PCA)
3.4. Linear Discriminate Analysis (LDA)
4. Proposed New Combination Method Based on Different Representation of Data
5. Experimental Results
6. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보