Name
Abstract | ||
|---|---|---|
Feature extraction is an important phase for image processing purposes since the output of the feature extraction is the input for classifiers. The importance of it applies to handwriting recognition problem, too. Distinctive features result in higher accuracy recognition of characters, or words. Therefore, it is crucial to be able to extract relevant and distinctive features from the image. In this study, we compare different feature extraction techniques for Hungarian handwriting recognition purpose. In order to be able to compare the techniques, the output of feature extraction phase is classifier using three classifiers namely, Support Vector Machines (SVM), Rough Sets Theory (RST) and Bayesian Networks (BN) using the WEKA machine learning tool. The results indicated that, the best classification results were retrieved using features calculated by the distribution of points in the image. However, it can be said that the combinations of different feature extraction types provide a greater deal of distinctiveness. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SACI.2018.8440952 | 2018 IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI) |
Keywords | Field | DocType |
WEKA machine learning tool,BN,image processing,feature extraction techniques,Hungarian handwriting recognition problem,image classification,character recognition,support vector machines,SVM,rough sets theory,RST,Bayesian networks | Pattern recognition,Computer science,Support vector machine,Image processing,Handwriting recognition,Feature extraction,Control engineering,Rough set,Bayesian network,Artificial intelligence,Classifier (linguistics),Optimal distinctiveness theory | Conference |
ISBN | Citations | PageRank |
978-1-5386-4641-0 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaye Ediboglu Bartos | 1 | 0 | 0.34 |
| Eva Hajnal | 2 | 0 | 0.34 |
| Yasar Hoscan | 3 | 0 | 0.34 |