Optimizing Javanese Numeral Recognition Using YOLOv8 Technology: An Approach for Digital Preservation of Cultural Heritage

Lukman Syafie; Huzain Azis; Fadhila Tangguh Admojo

doi:10.56705/ijodas.v6i1.239

Lukman Syafie Universiti Kuala Lumpur
Huzain Azis Universiti Kuala Lumpur
Fadhila Tangguh Admojo Universiti Kuala Lumpur

DOI: https://doi.org/10.56705/ijodas.v6i1.239

Keywords: Educational Technology, Handwriting Recognition, Javanese Script, Object Detection, YOLOv8

Abstract

Introduction: The preservation of Javanese script as part of Indonesia’s cultural heritage is increasingly urgent in the digital era, especially due to declining literacy among younger generations. This study aims to explore the effectiveness of YOLOv8, an advanced object detection algorithm, for recognizing handwritten Javanese numerals to support efforts in cultural digitization and education. Methods: A dataset of 2,790 handwritten Javanese numerals (0–9) was collected from 93 respondents. Each numeral was manually annotated using bounding boxes via the MakeSense.ai platform. The YOLOv8 model was trained using 80% of the data and validated on the remaining 20%. Training was performed in the PyTorch framework with data augmentation techniques to increase robustness. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP), along with visualization through confidence curves and confusion matrices. Results: The model achieved a high validation precision of 88.3%, recall of 89.1%, and mAP of 0.88 at IoU 0.90. F1-score peaked at a confidence threshold of 0.89, while certain numerals like 'six' and 'nine' achieved near-perfect detection. Visualizations confirmed the model’s ability to accurately classify and localize characters in both training and unseen data. Minor misclassifications occurred between visually similar numerals. Conclusions: YOLOv8 demonstrates high effectiveness in recognizing handwritten Javanese numerals and holds significant potential for digital heritage preservation. Future work should focus on expanding the dataset, improving generalization under varied conditions, and integrating this model into educational tools and augmented reality applications for interactive learning.

Downloads

Download data is not yet available.

References

G. G. Casas, “Automatic Detection and Counting of Stacked Eucalypt Timber Using the YOLOv8 Model,” Forests, vol. 14, no. 12, 2023, doi: 10.3390/f14122369.

X. Zhang, “Detection of Leg Diseases in Broiler Chickens Based on Improved YOLOv8 X-Ray Images,” IEEE Access, vol. 12, pp. 47385–47401, 2024, doi: 10.1109/ACCESS.2024.3382193.

V. R. del Castillo, “Adapting YOLOv8 as a Vision-Based Animal Detection System to Facilitate Herding,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14001. pp. 603–610, 2023, doi: 10.1007/978-3-031-40725-3_51.

W. Zhang, Z. Jia, C. Feng, and M. Chen, “YOLOv8-SC:Target Detection Algorithm of Live Detection System for Tensioning Clamps in High-Voltage Transmission Lines,” in 2023 8th International Conference on Control, Robotics and Cybernetics (CRC), Dec. 2024, pp. 177–181, doi: 10.1109/CRC60659.2023.10488490.

G. Zidani, “Optimizing Pedestrian Tracking for Robust Perception with YOLOv8 and Deep sort,” Appl. Comput. Sci., vol. 20, no. 1, pp. 72–84, 2024, doi: 10.35784/acs-2024-05.

J. Terven, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Machine Learning and Knowledge Extraction, vol. 5, no. 4. pp. 1680–1716, 2023, doi: 10.3390/make5040083.

E. Casas, “YOLOv5 vs. YOLOv8: Performance Benchmarking in Wildfire and Smoke Detection Scenarios,” J. Image Graph., vol. 12, no. 2, pp. 127–136, 2024, doi: 10.18178/joig.12.2.127-136.

D. Thakur, P. Pal, A. Jadhav, N. Kable, B. V, and S. Deshpande, “YOLOv8- Based Helmet and Vest Detection System for Safety Assessment,” in 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Sep. 2023, pp. 1–10, doi: 10.1109/NMITCON58196.2023.10275958.

V. R. Joseph and A. Vakayil, “SPlit: An Optimal Method for Data Splitting,” Technometrics, vol. 64, no. 2, pp. 166–176, Apr. 2022, doi: 10.1080/00401706.2021.1921037.

B. Gašparović, “Evaluating YOLOV5, YOLOV6, YOLOV7, and YOLOV8 in Underwater Environment: Is There Real Improvement?,” 2023 8th Int. Conf. Smart Sustain. Technol. Split. 2023, 2023, doi: 10.23919/SpliTech58164.2023.10193505.

C. Dewi, D. Manongga, Hendry, E. Mailoa, and K. D. Hartomo, “Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System,” Big Data Cogn. Comput., vol. 8, no. 1, p. 9, Jan. 2024, doi: 10.3390/bdcc8010009.

Z. Afrin, “YOLOv8 Based Object Detection for Self-driving Cars,” 2023 26th International Conference on Computer and Information Technology, ICCIT 2023. 2023, doi: 10.1109/ICCIT60459.2023.10441381.

F. Solimani et al., “Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity,” Comput. Electron. Agric., vol. 218, p. 108728, Mar. 2024, doi: 10.1016/j.compag.2024.108728.

J. C. Camacho, “Mask R-CNN and YOLOv8 Comparison to Perform Tomato Maturity Recognition Task,” Commun. Comput. Inf. Sci., vol. 1885, pp. 382–396, 2023, doi: 10.1007/978-3-031-45438-7_26.

G. Liu, J. C. Nouaze, P. L. Touko Mbouembe, and J. H. Kim, “YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3,” Sensors, vol. 20, no. 7, p. 2145, Apr. 2020, doi: 10.3390/s20072145.

I. A. Bakhri and H. P. Sidik, “Realtime Recognition of Handwritten Lontara Makassar Characters Using Yolov5 Algorithm,” in 2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Dec. 2022, pp. 309–313, doi: 10.1109/ICITISEE57756.2022.10057924.

C. Dewi, “Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System,” Big Data Cogn. Comput., vol. 8, no. 1, 2024, doi: 10.3390/bdcc8010009.

P. Zhang, W. Hou, D. Wu, B. Ge, L. Zhang, and H. Li, “Real-Time Detection of Small Targets for Video Surveillance Based on MS-YOLOv5,” in 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD), May 2023, pp. 690–694, doi: 10.1109/ICAIBD57115.2023.10206275.

M. R. A. Priyadi, “Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecting the Ripeness of Oil Palm Fruit Bunch,” 10th Int. Conf. ICT Smart Soc. ICISS 2023 - Proceeding, 2023, doi: 10.1109/ICISS59129.2023.10291928.

F. Dang, D. Chen, Y. Lu, and Z. Li, “YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems,” Comput. Electron. Agric., vol. 205, p. 107655, Feb. 2023, doi: 10.1016/j.compag.2023.107655.

M. Sportelli et al., “Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios,” Appl. Sci., vol. 13, no. 14, p. 8502, Jul. 2023, doi: 10.3390/app13148502.

Y. Gong, “Handwritten Chinese Character Recognition in Ancient Books Based on Improved YOLOv7,” 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024. pp. 1526–1532, 2024, doi: 10.1109/AINIT61980.2024.10581730.

Q. Sun, “Recognition Method for Handwritten Steel Billet Identification Number Based on Yolo Deep Convolutional Neural Network,” Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. pp. 5642–5646, 2020, doi: 10.1109/CCDC49329.2020.9164720.

M. I. S. Zabala, “Handwritten Text Recognition Algorithm,” IFMBE Proceedings, vol. 106. pp. 61–79, 2024, doi: 10.1007/978-3-031-61960-1_7.

J. Sarmah, “Performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification,” Lecture Notes in Networks and Systems, vol. 844. pp. 215–227, 2024, doi: 10.1007/978-981-99-8479-4_16.