TELKOMNIKA Telecommunication Computing Electronics and Control
Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition

TELKOMNIKA Telecommunication Computing Electronics and Control
Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition

Convolutional neural networks
Dropout
Javanese character recognition
Multilayer perceptron
Support vector machine

This research paper explores the hybrid models for Javanese character
recognition using 15600 characters gathered from digital and handwritten
sources. The hybrid model combines the merit of deep learning using
convolutional neural networks (CNN) to involve feature extraction and
a machine learning classifier using support vector machine (SVM).
The dropout layer also manages overfitting problems and enhances training
accuracy. For evaluation purposes, we also compared CNN models with
three different architectures with multilayer perceptron (MLP) models with
one and two hidden layer(s). In this research, we evaluated three variants of
CNN architectures and the hybrid CNN-SVM models on both the accuracy
of classification and training time. The experimental outcomes showed that
the classification performances of all CNN models outperform the
classification performances of both MLP models. The highest testing
accuracy for basic CNN is 94.2% when using model 3 CNN. The increment
of hidden layers to the MLP model just slightly enhances the accuracy.
Furthermore, the hybrid model gained the highest accuracy result of 98.35%
for classifying the testing data when combining model 3 CNN with the SVM
classifier. We get that the hybrid CNN-SVM model can enhance the
accuracy results in the Javanese characters recognition.

Diyah Utami Kusumaning Putri, Dinar Nugroho Pratomo, Azhari

http://telkomnika.uad.ac.id

Jun 02, 2022

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