Experimental application of a Japanese historical document image synthesis method to text line segmentation

Naoto Inuzuka, Tetsuya Suzuki

研究成果: Conference contribution

抄録

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a text line segmentation method based on machine learning in our transcription support system for handwritten Japanese historical document in Kana, and are searching for a data synthesis method of annotated document images because it is time consuming to manually annotate a large set of document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 In this paper, we report our synthesis method of annotated document images designed for a Japanese historical document. To compare manually annotated Japanese historical document images and annotated document images synthesized by the method as training data for an object detection algorithm YOLOv3, we conducted text line segmentation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.

本文言語English
ホスト出版物のタイトルICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods
編集者Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred
出版社SciTePress
ページ628-634
ページ数7
ISBN(電子版)9789897584862
出版ステータスPublished - 2021
イベント10th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2021 - Virtual, Online
継続期間: 2021 2 42021 2 6

出版物シリーズ

名前ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods

Conference

Conference10th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2021
CityVirtual, Online
Period21/2/421/2/6

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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