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
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Applied Sciences and Smart Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.