Image Detection Analysis for Javanese Character Using YOLOv9 Models

Hari Suparwito(1*),

(1) Sanata Dharma University
(*) Corresponding Author

Abstract


The Javanese script needs to be digitized to improve access and usage, especially among younger generations. Digitizing Javanese characters is crucial for preserving Javanese culture and traditions in the long term. This study aims to detect and recognize Javanese characters using the YOLOv9 algorithm, known for its ability to detect various object types, including Latin and non-Latin scripts. The dataset used consists of 85 images of complete Javanese script arranged in a 4x5 grid of different characters. The dataset was divided into a training dataset (75 images) and a validation dataset (10 images). All data pre-processing was done using Roboflow tools. Two experiments were conducted, varying the weights of the YOLOv9 algorithm model: YOLOv9-c and YOLOv9-c-converted. The research results showed that the YOLOv9-c model outperformed YOLOv9-c-converted, achieving a confidence level of over 80% and an mAP value of 0.95 in recognizing Javanese script images. In other words, the YOLOv9 model succeeded in detecting and recognizing Javanese scripts well

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References


D. Iskandar, S. Hidayat, U. Jamaludin, and S. M. Leksono, “Javanese script digitalization and its utilization as learning media: an etnopedagogical approach,” International Journal of Mathematics and Sciences Education, vol. 1, no. 1, 2023, pp. 21–30.

A. Susanto, I. U. W. Mulyono, C. A. Sari, E. H. Rachmawanto, and R. R. Ali, “Javanese character recognition based on k-nearest neighbor and linear binary pattern features,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2022, pp. 309–316.

F. T. Anggraeny, Y. V. Via, and R. Mumpuni, “Image preprocessing analysis in handwritten Javanese character recognition,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 2, 2023, pp. 860–867.

I. F. Katili, M. A. Soeleman, and R. A. Pramunendar, “Character Recognition of Handwriting of Javanese Character Image using Information Gain Based on the Comparison of Classification Method,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, 2023, pp. 193–200.

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, 2021, pp. 1308–1315.

A. Prasetiadi, J. Saputra, I. Kresna, and I. Ramadhanti, “YOLOv5 and U-Net-based Character Detection for Nusantara Script,” Jurnal Online Informatika, vol. 8, no. 2, 2023, pp. 232–241.

J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.

H. Sugiharto, “Aksara Jawa YOLO v5 Dataset.” [Online]. Available: https://www.kaggle.com/datasets/hermansugiharto/aksara-jawa-yolo-v5-dataset. [Accessed: May 04, 2024]

A. Budi, “Hanacaraka dan Makna Bijak di Baliknya.” [Online]. Available: https://www.goodnewsfromindonesia.id/2017/01/23/hanacaraka-dan-makna-bijak-di-baliknya. [Accessed: May 15, 2024]

M. Hussain, “YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection,” Machines, vol. 11, no. 7, 2023, p. 677.

C. Koylu, C. Zhao, and W. Shao, “Deep neural networks and kernel density estimation for detecting human activity patterns from geo-tagged images: A case study of birdwatching on flickr,” ISPRS Int J Geoinf, vol. 8, no. 1, 2019, p. 45.




DOI: https://doi.org/10.24071/ijasst.v6i1.8779

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