HOG and Zone Base Features for Handwritten Javanese Character Classification

Rismiyati,; Khadijah, Khadijah; Riyanto, Djalal Er

doi:10.1109/icicos.2018.8621781

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…Based on this research, the combination of the Backpropagation method with Chi2 provides the best accuracy reaching 98% for data that has been trained and 73% for data that has not been trained [3]. Another approach to the introduction of Javanese characters is to use machine learning [4]- [6] and deep learning [7]- [10]. Deep learning is used CNN by applying a certain model variation and Deep Neural Network (DNN).…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Method for Classification of Syllables in Javanese Script

Fauziah

Aprilianta²,

Rustamaji³

2021

Int. J. Artif. Intell. Robot.

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Javanese script is one of the languages which are a typical Javanese culture. Javanese script is seen in its use in writing the name of a particular agency or location that has historical and tourism value. The use of Javanese script in public places makes the existence of this script seen by many people, not only by the Javanese people. Some of them have difficulty recognizing the Javanese characters they encounter. One method of pattern recognition and image processing is Convolutional Neural Network (CNN). CNN is a method that uses convolution operations in performing feature extraction on images as a basis for classification. The process consists of initial data processing, classification, and syllable formation. The classification consists of 48 classes covering Javanese script types, namely basic letters (Carakan) and voice-modifying scripts (Sandhangan). It is tested with multi-class confusion matrix scenarios to determine the accuracy, precision, and recall of the built CNN model. The CNN architecture consists of three convolution layers with max-pooling operations. The training configuration includes a learning rate of 0.0001, and the number of filters for each convolution layer is 32, 64, and 128 filters. The dropout value used is 0.5, and the number of neurons in the fully-connected layer is 1,024 neurons. The average performance value of accuracy reached 87.65%, the average precision value was 88.01%, and the average recall value was 87.70%.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Method for Classification of Syllables in Javanese Script

Fauziah

Aprilianta²,

Rustamaji³

2021

Int. J. Artif. Intell. Robot.

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“…In image classification, there are some general stages: preprocessing, feature extraction and selection, and classification [7]. There are various method for preprocessing step on handwritten character such as denoising [8]- [13], dilation [14], [15], binarization [16], [17], skeletonization [18]. Some research ISSN: 2302-9285  Image preprocessing analysis in handwritten Javanese character recognition (Fetty Tri Anggraeny)…”

Section: Introductionmentioning

confidence: 99%

“…need feature extraction, such as horizontal and vertical profile image [16], [19], zoning method [11], [18], [20], histogram of oriented gradient (HOG) feature [18], mesh and local line direction (LLD) [17], and fast fourier transform (FFT) [19]. Artificial neural network (ANN) is one powerful machine learning and helpful for classification, clustering, pattern recognition, and prediction [17].…”

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confidence: 99%

“…Fauziah et al [14] obtained 87.5% of accuracy by using dilation, the Otsu method, canny edge, and contour as preprocessing and convolutional neural network (CNN) classifier. Rismiyati et al [18] obtained 88.45% of accuracy by using the HOG feature and support vector machine (SVM). Another Rismiyati et al [23] research did not use any preprocessing technique except grayscaling.…”

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Image preprocessing analysis in handwritten Javanese character recognition

2023

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The handwriting produced by each person is unique, so each person has a different stroke, even though they write the same letter. Handwritten Javanese is an exciting topic to study, in addition to scientific purposes and preserving Indonesian culture. The Javanese character image dataset is aksara Jawa: aksara Jawa custom dataset from the Kaggle database consists of 2,154 train data and 480 evaluation data. This research proposed to analyze the impact of some preprocessing methods in recognizing handwritten Javanese characters. The preprocessing methods are dilation, skeletonization, and noise reduction. The first process is segmentation for region of interest (ROI) extraction, then various preprocessing is used, and finally, the recognition step neural network (NN) to measure the effectiveness of the preprocessing method. The experiment shows that all preprocessing methods (dilation, skeletonization, and noise reduction) give excellent results, especially on the black background color, reaching 98% accuracy. Other experimental findings show that in any preprocessing combination, the black background accuracy is better than the white one.

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“…Their applications, however, must be reviewed due to limited data and unsatisfactory performance. Finally, several feature extraction methods have also been extensively explored in [19]- [21]. The results show that by employing feature extraction, some traditional machine learning methods such as KNN are able to produce fairly good accuracy of more than 80%.…”

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confidence: 99%

Classification of handwritten Javanese script using random forest algorithm

et al. 2021

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The ability to read and write Javanese scripts is one of the most important competencies for students to have in order to preserve the Javanese language as one of the Indonesian cultures. In this study, we developed a predictive model for 20 Javanese characters using the random forest algorithm as the basis for developing Javanese script learning media for students. In building the model, we used an extensive handwritten image dataset and experimented with several different preprocessing methods, including image conversion to black-and-white, cropping, resizing, thinning, and feature extraction using histogram of oriented gradients. From the experiment, it can be seen that the resulting random forest model is able to classify Javanese characters very accurately with accuracy, precision, and recall of 97.7%.

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HOG and Zone Base Features for Handwritten Javanese Character Classification

Cited by 4 publications

References 10 publications

Convolutional Neural Network Method for Classification of Syllables in Javanese Script

Convolutional Neural Network Method for Classification of Syllables in Javanese Script

Image preprocessing analysis in handwritten Javanese character recognition

Classification of handwritten Javanese script using random forest algorithm

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