Computer Science ›› 2015, Vol. 42 ›› Issue (Z6): 102-106, 121.

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Comparison of Several Classification Approaches to Digit and Letter Recognition:Experimental Results

CHEN Ai-xiang   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Classification is an important problem in machine learning.This paper built several image datasets consisting of ten digits and 26 upper case and 26 lower case english letter to evaluate the performace of six classifiers,SVM(Support Vecter Machine),NB(Nave Bayes),RT(Random Tree),MLP(Multi Layer Perception),BOOST,Knearest.Experimental results show SVM has better generalization ability,while NB and MLP are more sensitive to datasets.In addition,the recognition accuracy of our system based on SVM reaches to 94.2191%,which is better than many publicly reported results in the literature.

Key words: Machine learning,Classifier,Dataset,Performance comparsion,Recognition accuracy

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