DOCUMENT SHADOW REMOVAL WITH FOREGROUND DETECTION LEARNING FROM FULLY SYNTHETIC IMAGES

Yuhi Matsuo, Naofumi Akimoto, Yoshimitsu Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Computer Society
Pages1656-1660
Number of pages5
ISBN (Electronic)9781665496209
DOIs
Publication statusPublished - 2022
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: 2022 Oct 162022 Oct 19

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period22/10/1622/10/19

Keywords

  • deep neural networks
  • document images
  • Shadow removal

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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