An Initial Study in Historical Javanese Script Recognition Data Synthetic using Neural Style Transfer

Recognizing and digitizing Javanese scripts is challenging due to their unique characters and style, which traditional character recognition methods struggle to handle. This study uses neural style transfer with a pretrained VGG-19 model to create synthetic data, for improving character recognition accuracy. High-resolution scans of Javanese documents and printed fonts were used, split into training, validation, and test sets. Following this, a YOLOv9 object detection model was trained to identify and classify Javanese characters within the augmented datasets. The results indicate that the use of style transfer enhances detection performance, achieving a precision of 92.8%, recall of 91.5%, mAP50 of 92.4%, and mAP50-94 of 81.7%, outperforming previous methods. This demonstrates that the inclusion of style transfer and synthetic data generation increase the model's accuracy in recognizing complex historical texts.