Complex image processing with less data—Document image binarization by integrating multiple pre-trained U-Net modules

作者：

Highlights：

• Propose a novel document binarization method by cascading pre-trained U-Nets.

• Use pre-trained U-Net for solving a training image shortage problem.

• Study for optimal inter-module skip-connections between U-Net modules.

• Analyze the results of DIBCO images included various types of noise.

• Compare all DIBCO dataset (2009–2018) and show robust performance.

摘要

•Propose a novel document binarization method by cascading pre-trained U-Nets.•Use pre-trained U-Net for solving a training image shortage problem.•Study for optimal inter-module skip-connections between U-Net modules.•Analyze the results of DIBCO images included various types of noise.•Compare all DIBCO dataset (2009–2018) and show robust performance.

论文关键词：Convolutional neural network,U-Net,Document image binarization,DIBCO,H-DIBCO

论文评审过程：Received 5 February 2020, Revised 8 June 2020, Accepted 4 August 2020, Available online 7 August 2020, Version of Record 11 August 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107577