DOCUMENT SHADOW REMOVAL WITH FOREGROUND DETECTION LEARNING FROM FULLY SYNTHETIC IMAGES

Yuhi Matsuo, Naofumi Akimoto, Yoshimitsu Aoki

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Shadow removal for document images is a major task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large-scale and diverse dataset is laborious and remains a great challenge. Thus, only small real datasets are available. To create relatively large datasets, a graphic renderer has been used to synthesize shadows, nonetheless, it is still necessary to capture real documents. Thus, the number of unique documents is limited, which negatively affects a network's performance. In this paper, we present a large-scale and diverse dataset called fully synthetic document shadow removal dataset (FSDSRD) that does not require capturing documents. The experiments showed that the networks (pre-)trained on FSDSRD provided better results than networks trained only on real datasets. Additionally, because foreground maps are available in our dataset, we leveraged them during training for multitask learning, which provided noticeable improvements. The code is available at: https://github.com/IsHYuhi/DSRFGD.

本文言語English
ホスト出版物のタイトル2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
出版社IEEE Computer Society
ページ1656-1660
ページ数5
ISBN(電子版)9781665496209
DOI
出版ステータスPublished - 2022
イベント29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
継続期間: 2022 10月 162022 10月 19

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
ISSN(印刷版)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
国/地域France
CityBordeaux
Period22/10/1622/10/19

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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