Published: 2025-08-01
Image Segmentation of East OKU Script Using the Bounding Box Method for Cultural Heritage Digitization
DOI: 10.35870/ijsecs.v5i2.4045
Downloads
Abstract
East Ogan Komering Ulu (OKU) is distinguished by its cultural heritage, which encompasses historical artifacts such as traditional houses, crafts, and ceremonial dances. Among the most significant cultural assets are relics inscribed with ancient scripts, including Pallawa and Ulu, which offer valuable insight into the region’s historical literacy. The present study addresses the segmentation of OKU Timur script images through the Bounding Box method. This approach was selected based on its practicality and efficiency, particularly in the context of datasets where script characters exhibit straightforward forms and the overall data volume remains manageable. The segmentation process utilizes Python within the Google Colaboratory platform, ensuring accessible and reproducible workflows. Accurate segmentation is essential to support ongoing digitization and preservation of cultural scripts. The methodology involves gathering data from local artifacts, converting images to binary format, and isolating characters using Bounding Boxes. The results demonstrate that the method effectively separates individual script characters, laying the groundwork for dataset development and subsequent image classification tasks.
Keywords
OKU Timur Script ; Bounding Box ; Image Segmentation ; Python ; Google Colaboratory ; Dataset
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 5 No. 2 (2025)
-
Section: Articles
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2025 Authors
-
DOI: 10.35870/ijsecs.v5i2.4045
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
M Fikri
Intelligent Systems Research Group (ISRG), Faculty of Science Technology, Universitas Bina Darma, Palembang City, South Sumatra Province, Indonesia
Ilman Zuhri Yadi
Intelligent Systems Research Group (ISRG), Faculty of Science Technology, Universitas Bina Darma, Palembang City, South Sumatra Province, Indonesia
Yesi Novaria Kunang
Intelligent Systems Research Group (ISRG), Faculty of Science Technology, Universitas Bina Darma, Palembang City, South Sumatra Province, Indonesia
Leon Andretti Abdillah
Intelligent Systems Research Group (ISRG), Faculty of Science Technology, Universitas Bina Darma, Palembang City, South Sumatra Province, Indonesia
-
Setiyawan, P. A., & Bayupati, I. P. A. (2014). Balinese Alphabet Sebagai Aplikasi Media Pembelajaran Aksara Bali Berbasis Android Mobile Platform. Jurnal Merpati, 2(2), 226–237.
-
Adipranata, R., Siwalankerto, J., & Telp, S. (2014). Kombinasi metode morphological gradient dan transformasi watershed pada proses segmentasi citra digital. Jurnal Informatika Petra, (031).
-
Budiman, A., Fadlil, A., & Umar, R. (2023). Improving the results of learning nglegena Javanese handwriting using backpropagation artificial neural network. Edunesia Jurnal Ilmiah Pendidikan, 4(1), 259–269. https://doi.org/10.51276/edu.v4i1.339
-
Darma, I. W. A. S., & Ariasih, N. K. (2018). Handwritten Balinesse Character Recognition using K-Nearest Neighbor. International Association for Convergence Science & Technology, 119-123. https://doi.org/10.31227/osf.io/z6m8u
-
Darma, I. W. A. S. (2019). Implementation of Zoning and K-Nearest Neighbor in Character Recognition of Wrésastra Script. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, 10(1), 9. https://doi.org/10.24843/lkjiti.2019.v10.i01.p02
-
Kantorov, V., Oquab, M., Cho, M., & Laptev, I. (2016, September). Contextlocnet: Context-aware deep network models for weakly supervised localization. In European conference on computer vision (pp. 350-365). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-46454-1_22
-
Kaur, S., & Sagar, B. B. (2023). Efficient Scalable Template-Matching Technique for Ancient Brahmi Script Image. Computers, Materials & Continua, 75(1), 1541–1559. https://doi.org/10.32604/cmc.2023.032857
-
Mathew, C. J., Shinde, R. C., & Patil, C. Y. (2015, March). Segmentation techniques for handwritten script recognition system. In 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015] (pp. 1-7). IEEE. https://doi.org/10.1109/iccpct.2015.7159397
-
Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE access, 8, 142642-142668. https://doi.org/10.1109/access.2020.3012542
-
Papadopoulos, D. P., Uijlings, J. R., Keller, F., & Ferrari, V. (2016). We don't need no bounding-boxes: Training object class detectors using only human verification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 854-863). https://doi.org/10.1109/cvpr.2016.99
-
Rasyidi, M. A., Bariyah, T., Riskajaya, Y. I., & Septyani, A. D. (2021). Classification of handwritten Javanese script using random forest algorithm. Bulletin of Electrical Engineering and Informatics, 10(3), 1308-1315. https://doi.org/10.11591/eei.v10i3.3036
-
Sapitri, W., Kunang, Y. N., Yadi, I. Z., & Mahmud, M. (2023). The Impact of Data Augmentation Techniques on the Recognition of Script Images in Deep Learning Models. Jurnal Online Informatika, 8(2), 169-176. https://doi.org/10.15575/join.v8i2.1073
-
Gumiwang, Z. Y. M., Zahhar, A. H. N., & Maulana, H. (2023). Perbandingan Segmentasi Citra Menggunakan Algoritma K-Means Dan Algoritma Fuzzy C-Means. Jurnal Manajamen Informatika Jayakarta, 3(1), 21-26. http://journal.stmikjayakarta.ac.id/index.php/JMIJayakarta
-
Sari, N. L. K., Hartoyo, P., & Ajrun, A. (2022). Analisis Karakteristik Segmen Pada Citra Mamografi Dengan Menggunakan Metode Segmentasi Watershed. JURNAL PEMBELAJARAN FISIKA, 11(2), 59-64. https://doi.org/10.19184/jpf.v11i2.31643
-
Septiarini, A. (2016). Segmentasi Karakter Menggunakan Profil Proyeksi. Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, 7(2), 66-69.
-
Maedjaja, F. (2021). Sistem Deteksi Teks pada Cover Buku dengan Pendekatan Karakter Teks. Infact: International Journal of Computers, 6(2).
-
Riandini, R., & Kuncoro, D. (2023). Estimasi Panjang Antrean Kendaraan pada Persimpangan Jalan Raya dengan Sensor Kamera Menggunakan Metode Queue Length Estimation. Journal of Computer Engineering, Network, and Intelligent Multimedia, 1(1), 14-20. https://doi.org/10.59378/jcenim.v1i1.4
-
Sutama, V. M., Magdalena, R., & Wijayanto, I. (2018). Identifikasi Objek Dominan Citra Digital Menggunakan Metode Markov Random Field (mrf). eProceedings of Engineering, 5(3), 4859–4865. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/7839
-
Hamanrora, M. D., Kunang, Y. N., Yadi, I. Z., & Mahmud. (2024). Image segmentation of Komering script using bounding box. Indonesian Journal of Electrical Engineering and Computer Science, 35(3), 1565–1578. https://doi.org/10.11591/ijeecs.v35.i3.pp1565-1578
-
Nainggolan, I. B., Magdalena, R., & Fuadah, R. Y. N. (2018). Matched Filter Dan Operasi Morfologi Untuk Estimasi Derajat Kebengkokan Tulang. eProceedings of Engineering, 5(3), 5108.
-
Rahmadhika, M. K., & Thantawi, A. M. (2021). Rancang Bangun Aplikasi Face Recognition Pada Pendekatan CRM Menggunakan Opencv Dan Algoritma Haarcascade. IKRA-ITH INFORMATIKA: Jurnal Komputer Dan Informatika, 5(1), 109-118.
-
Maesaroh, M., Padilah, T. N., & Jaman, J. H. (2023). Penerapan Algoritma K-Means Clustering Pada Pengelompokan Daerah Penyebararan Diare Di Provinsi Jawa Barat. JATI (Jurnal Mahasiswa Teknik Informatika), 7(4), 2783-2787. https://doi.org/10.36040/jati.v7i4.7208.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.