DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character
Keywords:
Convolution Neural Network, Character Recognition, Cultural Heritage Preservation, Densenet, Javanese Hanacaraka Script
Abstract
This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese
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[5] N. P. Sutramiani, N. Suciati, and D. Siahaan, “Transfer Learning on Balinese Character Recognition of Lontar Manuscript Using
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2020, doi: 10.1109/ICICoS51170.2020.9299030.
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hardware,” Frontiers in Neuroscience, vol. 16, 2022, doi: 10.3389/fnins.2022.951164.
[9] U. BS and U. K, “a Review Paper on Ocr Using Convolutional Neural Networks,” International Journal of Engineering Applied
Sciences and Technology, vol. 7, no. 7, pp. 102–106, 2022, doi: 10.33564/ijeast.2022.v07i07.018.
[10] M. F. Muhdalifah, “Pooling Comparison in CNN Architecture for Javanese Script Classification,” International Journal of
Informatics and Computation, vol. 3, no. 2, p. 15, 2022, doi: 10.35842/ijicom.v3i2.30.
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[12] M. A. Rasyidi, T. Bariyah, Y. I. Riskajaya, and A. D. Septyani, “Classification of handwritten javanese script using random
forest algorithm,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 3, pp. 1308–1315, 2021, doi:
10.11591/eei.v10i3.3036.
[13] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,” Evolutionary
Intelligence, vol. 15, no. 1, pp. 1–22, 2022, doi: 10.1007/s12065-020-00540-3.
[14] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, A survey of the recent architectures of deep convolutional neural networks.
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[15] Purwono, A. Ma’arif, W. Rahmaniar, H. I. K. Fathurrahman, A. Z. K. Frisky, and Q. M. U. Haq, “Understanding of Convolutional
Neural Network (CNN): A Review,” International Journal of Robotics and Control Systems, vol. 2, no. 4, pp. 739–748,
2022, doi: 10.31763/ijrcs.v2i4.888.
[16] S. Singh and D. Schicker, “Seven Basic Expression Recognition Using ResNet-18,” pp. 1–3, 2021.
[17] D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” 2019 IEEE 10th Annual Ubiquitous
Computing, Electronics and Mobile Communication Conference, UEMCON 2019, pp. 0280–0285, 2019, doi: 10.1109/UEMCON47517.2019.8993089.
[18] D. Gupta, A. Gupta, R. Gandhi, P. Vishwavidyalaya, and S. S. Gupta, “Identification of Alzheimer’s disease from MRI image
employing a probabilistic deep learning-based approach and the VGG16,” 2023.
[19] W. Gong, X. Zhang, B. Deng, and X. Xu, “Palmprint recognition based on convolutional neural network-alexnet,” Proceedings
of the 2019 Federated Conference on Computer Science and Information Systems, FedCSIS 2019, vol. 18, pp. 313–316, 2019,
doi: 10.15439/2019F248.
[20] N. S. Shadin, S. Sanjana, and N. J. Lisa, “COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(
CNN) and InceptionV3,” 2021 International Conference on Information Technology, ICIT 2021 - Proceedings, vol. 3, no.
September 2012, pp. 799–804, 2021, doi: 10.1109/ICIT52682.2021.9491752.
[21] G. S. Nugraha, M. I. Darmawan, and R. Dwiyansaputra, “Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16,
Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition,” Kinetik: Game Technology, Information System, Computer
Network, Computing, Electronics, and Control, vol. 4, no. 2, 2023, doi: 10.22219/kinetik.v8i2.1667.
[22] Z. Zhong, M. Zheng, H. Mai, J. Zhao, and X. Liu, “Cancer image classification based on DenseNet model,” Journal of Physics:
Conference Series, vol. 1651, no. 1, 2020, doi: 10.1088/1742-6596/1651/1/012143.
[23] G. Huang, Z. Liu, G. Pleiss, L. Van Der Maaten, and K. Q. Weinberger, “Convolutional Networks with Dense Connectivity,”
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 8704–8716, 2022, doi:
10.1109/TPAMI.2019.2918284.
[24] Z. Shi, M. Chen, and Z. Wu, “Hyperspectral Image Classification Based on Dual-Scale Dense Network with Efficient Channel
Attentional Feature Fusion,” Electronics (Switzerland), vol. 12, no. 13, 2023, doi: 10.3390/electronics12132991.
[25] S. A. Albelwi, “Deep Architecture based on DenseNet-121 Model for Weather Image Recognition,” International Journal of
Advanced Computer Science and Applications, vol. 13, no. 10, pp. 559–565, 2022, doi: 10.14569/IJACSA.2022.0131065.
[26] E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose
COVID-19 in X-Ray Images,” 2020.
[27] M. M. Hasan, H. Ali, M. F. Hossain, and S. Abujar, “Preprocessing of Continuous Bengali Speech for Feature Extraction,” 2020
11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, pp. 1–4, 2020,
doi: 10.1109/ICCCNT49239.2020.9225469.
Published
2024-03-28
How to Cite
Sudewo, E. D., Biddinika, M., & Fadlil, A. (2024). DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(2), 453-464. https://doi.org/https://doi.org/10.30812/matrik.v23i2.3855
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