Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition

Abstract

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.

A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19

“…This shows the VGG16 and VGG19 models built had a good performance in predicting all classes and had a good balance value between precision, and recall. Based on the research results obtained by [44] regarding the Javanese script classification process using the model results of the combination of CNN and SVM, the test accuracy result is 98.35%, this proves the model built in this study can perform the classification process very well, especially with models use transfer learning VGG16 and VGG19. As for the support value in all models is 400, because the support value is the total amount of data used to test the model.…”

A CNN-KNN Based Recognition of Online Handwritten Symbols within Physics Expressions Using Contour-Based Bounding Box (CBBS) Segmentation Technique

“…The most notable outcome was achieved by the hybrid model, which combined the third CNN architecture with the SVM classifier, resulting in an impressive accuracy rate of 98.35% in classifying the testing data. This amalgamated CNN-SVM approach effectively elevates the accuracy of Javanese character recognition, showcasing its potential for enhancement in this domain (Putri et al, 2023). A robust approach for the classification of handwritten musical symbols using Convolution Neural Networks (CNN), k-nearest Neighbor (kNN), Support Vector Machine (SVM) and Random Forest (RaF) is proposed and three network topologies namely a baseline network, generic object recognition and HMSnet (Baró et al, 2019).…”

Handwritten Character Recognition in Assyrian Language Using Convolutional Neural Network

“…The Convolutional Neural Network (CNN) architecture is a more sophisticated iteration of the multi-layer perceptron (MLP) foundation. The functionality of the CNN framework has similarities to that of the human brain (Putri, Pratomo, & Azhari, 2023). Humans use their naked eyes to discover and distinguish items by viewing hundreds of objects (Albahli, Nawaz, Javed, & Irtaza).…”