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Deep Learning Approaches for Nusantara Scripts Optical Character Recognition
https://doi.org/10.22146/ijccs.86302 Agi Prasetiadi(1*), Julian Saputra(2), Iqsyahiro Kresna(3), Imada Ramadhanti(4)
(1) Institut Teknologi Telkom Purwokerto
(2) Institut Teknologi Telkom Purwokerto
(3) Institut Teknologi Telkom Purwokerto
(4) Institut Teknologi Telkom Purwokerto
(*) Corresponding Author
Abstract
The number of speakers of regional languages who are able to read and to write traditional scripts in Indonesia is decreasing. If left unaddressed, this will lead to the extinction of Nusantara scripts and it is not impossible that their reading methods will be forgotten in the future. To anticipate this, this study aims to preserve the knowledge of reading ancient scripts by developing a Deep Learning model that can read document images written using one of the 10 Nusantara scripts we have collected: Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese. While previous studies have made efforts to read traditional Nusantara scripts using various Machine Learning and Convolutional Neural Network algorithms, they have primarily focused on specific scripts and lacked an integrated approach from script type recognition to character recognition. This study is the first to comprehensively address the entire range of Nusantara scripts, encompassing script type detection and character recognition. Convolutional Neural Network, ConvMixer, and Visual Transformer models were utilized and their respective performances were compared. The results demonstrate that our models achieved 96% accuracy in classifying Nusantara script types, with character recognition accuracy ranging from 93% to approximately 100% across the ten scripts.
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S. Dardjowidjojo, ``Strategies for a successful national language policy: The Indonesian case,'' Int. J. Sociol. Lang., vol. 130, no. 130, pp. 35-47, 1998, doi: 10.1515/ijsl.1998.130.35.
D. A. Maharani, M. F. Pratama, and D. Setiawati, ``Cuneiform (Huruf Paku) Sebagai Pelopor Lahirnya Aksara Di Nusantara,'' Jurnal Sejarah, Budaya dan Pengajarannya, vol. 1, no. 2, pp. 1-11, 2022.
M. I. Romadhan, ``Festival Sebagai Media Komunikasi Dalam Membangun Citra Destinasi Wisata Budaya Di Sumenep,'' Destinesia: Jurnal Hospitaliti dan Pariwisata, vol. 1, no. 1, pp. 1-10, 2019. doi: 10.31334/jd.v1i1.549.
A. Mawadda Warohma, P. Kurniasari, S. Dwijayanti, Irmawan and B. Yudho Suprapto, ``Identification of Regional Dialects Using Mel Frequency Cepstral Coefficients (MFCCs) and Neural Network,'' in 2018 International Seminar on Application for Technology of Information and Communication, Semarang, Indonesia, 2018, pp. 522-527, doi: 10.1109/ISEMANTIC.2018.8549731.
J. Lo Bianco, ``The importance of language policies and multilingualism for cultural diversity,'' Int. Soc. Sci. J., vol. 61, no. 199, pp. 37-67, 2010, doi: 10.1111/j.1468-2451.2010.01747.x.
E. Alfian, ``Penggunaan Unsur Aksara Nusantara Pada Huruf Modern,'' Jurnal Komunikasi Visual, vol. 7, no. 1, pp. 42-48, 2014.
P. K. Charles, V. Harish, M. Swathi, and C. H. Deepthi, ``A review on the various techniques used for optical character recognition,'' International Journal of Engineering Research and Applications, vol. 2, no. 1, pp. 659-662, 2012.
A. Ghazi, ``The urgency of electronic Know Your Customer (e-KYC): How electronic customer identification works to prevent money laundering in the fintech industry,'' Diponegoro Law Review, vol. 7, no. 1, pp. 34-52, 2018.
A. W. Mahastama and L. D. Krisnawati, ``Optical character recognition for printed javanese script using projection profile segmentation and nearest centroid classifier,'' in 2020 Asia Conference on Computers and Communications, ACCC 2020, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 52-56. doi: 10.1109/ACCC51160.2020.9347895.
A. R. Widiarti, ``Penelitian Pendahuluan Transliterasi Citra Aksara Bali Menggunakan Ciri Momen Invarian dan Algoritma Klasifikasi SVM atau CNN,'' JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 10, no. 1, pp. 580-589, 2023.
R. Maulana, ``Aksara-aksara di Nusantara: Seri Ensiklopedia,'' Samudra Biru, 2020.
G. K. Javaholic, ``Gaul Aksara Jawa,'' LKIS Pelangi Aksara, 2015.
S. R. Saragih and P. Utomo, ``Penarapan Algoritma Prefix Code Dalam Kompresi Data Teks,'' in Proceedings of the 2020 4th International Conference on Computer Science and Computational Intelligence (ICCCI), 2020, pp. 1-4, doi: 10.30865/komik.v4i1.2691.
N. Ibrahim, G. A. Lestary, F. S. Hanafi, K. Saleh, N. K. C. Pratiwi, M. S. Haq, and A. I. Mastur, ``Klasifikasi Tingkat Kematangan Pucuk Daun Teh menggunakan Metode Convolutional Neural Network,'' ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 10, no. 1, p. 162, 2022.
K. H. Mahmud and S. Al Faraby, ``Klasifikasi Citra Multi-Kelas Menggunakan Convolutional Neural Network,'' in Proceedings of the International Conference on Engineering and Optimization (ICEO), 2019, pp. 2127-2136. doi: 10.34818/eoe.v6i1.8501.
T. M. Hafiez, D. Iskandar, A. W. SK, and R. F. Boangmanalu, ``Optimasi Klasifikasi Gambar Varietas Jenis Tomat dengan Data Augmentation dan Convolutional Neural Network,'' Smart Comp: Jurnalnya Orang Pintar Komputer, vol. 11, no. 2, pp. 175-186, 2022.
G. F. Fitriana, A. B. Arifa, A. Prasetiadi, F. D. Adhinata, and N. G. Ramadhan, "Improving Accuracy of Cloud Images Using DenseNet-VGG19," International Journal on Advanced Science, Engineering & Information Technology, vol. 13, no. 2, 2023.
F. Siddique, S. Sakib, and M. A. B. Siddique, ``Recognition of handwritten digit using convolutional neural network in Python with TensorFlow and comparison of performance for various hidden layers,'' in 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), pp. 541-546, September 2019.
C. Garbin, X. Zhu, and O. Marques, ``Dropout vs. batch normalization: an empirical study of their impact to deep learning,'' Multimedia Tools and Applications, vol. 79, pp. 12777-12815, 2020.
W. A. Haque, S. Arefin, A. S. M. Shihavuddin, and M. A. Hasan, ``DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements,'' Expert Systems with Applications, vol. 168, p. 114481, 2021.
A. Trockman and J. Z. Kolter, ``Patches are all you need?,'' in arXiv preprint, arXiv:2201.09792, 2022.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Schmidhuber, and N. Houlsby, ``An image is worth 16x16 words: Transformers for image recognition at scale,'' arXiv preprint, arXiv:2010.11929, 2020.
K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, Z. Yang, Y. Zhang, and D. Tao, ``A Survey on Vision Transformer,'' arXiv:2012.12556, 2020.
Y. Liu, Y. Zhang, Y. Wang, F. Hou, J. Yuan, J. Tian, Y. Zhang, Z. Shi, J. Fan, and Z. He, ``A Survey of Visual Transformers,'' arXiv:2111.06091, 2021.
DOI: https://doi.org/10.22146/ijccs.86302
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