Comparison of bidirectional associative memory, counterpropagation and evolutionary neural network for Java characters recognition

Budhi, Gregorius Satia; Adipranata, Rudy

doi:10.1109/icaicta.2014.7005906

Cited by 7 publications

(5 citation statements)

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“…This application can later be combined with the result of the present research to create optical character recognition and an editor for Javanese characters. This research is an extension of previous research [4], [5], which evaluated the use of a backpropagation neural network for character recognition and some improvement in digital image preprocessing.…”

Section: Related Workmentioning

confidence: 98%

Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

Budhi

Adipranata

2015

J. ICT.Res.Appl.

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Abstract. Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.

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Section: Related Workmentioning

confidence: 98%

Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

Budhi

Adipranata

2015

J. ICT.Res.Appl.

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“…Nowadays, the use of Javanese script just can be found in historical relics or wall cravings. Sometimes, it also can be used in place name signboards, street signboards, or decorations as the transcription for the Roman alphabet [2].…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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“…Next we will focus on feature extraction and classification methods Several classification methods used in previous studies are k-NN [3], [4], neural networks [4]- [7], CNN [7]- [9], and SVM [10]. And several feature extraction in handwritten Javanese letter are zoning [11], ICZ-ZCZ [12]- [14], LBP [10], [15], [16], and HOG [17], [18]. Budhi [9].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Fetty Tri Anggraeny,

Yisti Vita Via,

Retno Mumpuni

et al. 2023

Technium

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Indonesia consists of various regional tribes, where each tribe has cultural diversity and some even have their own regional letters, like Javanese tribe has Javanese characters. Javanese letters consist of 20 basic letters called Nglegena script. Subject about Javanese language is delivered to elementary student until now aims to preserve Indonesian culture especially the Javanese. In this study, we present two feature extraction methods are Local Binary Pattern (LBP) and Profile Projection (PP). Neural Network (NN) chosen as classifiers for classifying 20 javanese letters Nglegena. Some digital image processing processes are carried out, are image inversion, dilation, denoising and skeletoning. The Javanese script dataset is taken from the Kaggle database with the name Aksara Jawa: Aksara Jawa Custom Dataset, consists of 2154 train images and 480 test images. The experiment were carried out in two models, Projection Profile Histogram - Neural Network (PPH-NN) and Local Binary Pattern Histogram - Neural Network (LBPH-NN). The experiment show that both feature extraction methods have very good performance, 99.98% PPH-NN and 89.6% LBPH-NN on average.

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Comparison of bidirectional associative memory, counterpropagation and evolutionary neural network for Java characters recognition

Cited by 7 publications

References 1 publication

Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

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

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

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