Experimental Application of a Japanese Historical Document Image Synthesis Method to Text Line Segmentation

We plan to use a text line segmentation method based on machine learning in our transcription support system for handwritten Japanese historical document in Kana, and are searching for a data synthesis method of annotated document images because it is time consuming to manually annotate a large set of document images for training data for machine learning. In this paper, we report our synthesis method of annotated document images designed for a Japanese historical document. To compare manually annotated Japanese historical document images and annotated document images synthesized by the method as training data for an object detection algorithm YOLOv3, we conducted text line segmentation experiments using the object detection algorithm. The experimental results show that a model trained by the synthetic annotated document images are competitive with that trained by the manually annotated document images from the view point of a metric intersection-over-union.