Comparative Study of Different Classification Models on Benchmark Dataset of Handwritten Meitei Mayek Characters
| Autor: | Deena Hijam, Sarat Saharia |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Computer Science::Neural and Evolutionary Computation Decision tree Pattern recognition Optical character recognition computer.software_genre Convolutional neural network Random forest Support vector machine Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Multilayer perceptron Benchmark (computing) Artificial intelligence business computer |
| Zdroj: | International Conference on Intelligent Computing and Smart Communication 2019 ISBN: 9789811506321 |
| DOI: | 10.1007/978-981-15-0633-8_7 |
| Popis: | This paper reports a comparative study of seven popular classification models namely decision tree, KNN, Linear Support Vector Classifier, Multilayer Perceptron, Random Forest, Support Vector Machine, and Gaussian Naive Bayes on a benchmark dataset of handwritten Meitei Mayek characters. Three different architectures of Convolutional Neural Network are also studied and results show that CNN model achieves state-of-the-art accuracy of 98.11% on the concerned dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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