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SealGAN: 基于生成式对抗网络的印章消除研究

李新利 邹昌铭 杨国田 刘禾

李新利, 邹昌铭, 杨国田, 刘禾. SealGAN: 基于生成式对抗网络的印章消除研究. 自动化学报, 2021, 47(11): 2614−2622 doi: 10.16383/j.aas.c190459
引用本文: 李新利, 邹昌铭, 杨国田, 刘禾. SealGAN: 基于生成式对抗网络的印章消除研究. 自动化学报, 2021, 47(11): 2614−2622 doi: 10.16383/j.aas.c190459
Li Xin-Li, Zou Chang-Ming, Yang Guo-Tian, Liu He. SealGAN: Research on the seal elimination based on generative adversarial network. Acta Automatica Sinica, 2021, 47(11): 2614−2622 doi: 10.16383/j.aas.c190459
Citation: Li Xin-Li, Zou Chang-Ming, Yang Guo-Tian, Liu He. SealGAN: Research on the seal elimination based on generative adversarial network. Acta Automatica Sinica, 2021, 47(11): 2614−2622 doi: 10.16383/j.aas.c190459

SealGAN: 基于生成式对抗网络的印章消除研究

doi: 10.16383/j.aas.c190459
详细信息
    作者简介:

    李新利:华北电力大学控制与计算机工程学院副教授. 主要研究方向为模式识别与智能系统, 图像处理, 燃烧过程检测技术. 本文通信作者.E-mail: lixinli@ncepu.edu.cn

    邹昌铭:华北电力大学控制与计算机工程学院硕士研究生. 主要研究方向为深度学习, 图像处理.E-mail: 1172227195@ncepu.edu.cn

    杨国田:华北电力大学控制与计算机工程学院教授. 主要研究方向为智能机器人, 计算机视觉, 火力发电精细化燃烧与优化控制.E-mail: ygt@ncepu.edu.cn

    刘禾:华北电力大学控制与计算机工程学院教授. 主要研究方向为图像处理, 计算机视觉, 模式识别.E-mail: lh@ncepu.edu.cn

SealGAN: Research on the Seal Elimination Based on Generative Adversarial Network

More Information
    Author Bio:

    LI Xin-Li Associate professor at the School of Control and Computer Engineering, North China Electric Power University. Her research interest covers pattern recognition and intelligent system, image processing, combustion process detection technology. Corresponding author of this paper

    ZOU Chang-Ming Master student at the School of Control and Computer Engineering, North China Electric Power University. His research interest covers deep learning and image processing

    YANG Guo-Tian Professor at the School of Control and Computer Engineering, North China Electric Power University. His research interest covers intelligent robot, computer vision, fine combustion and optimal control of thermal power plant

    LIU He Professor at the School of Control and Computer Engineering, North China Electric Power University. His research interest covers image processing, computer vision, pattern recognition

  • 摘要: 发票是财务系统的重要组成部分. 随着计算机视觉和人工智能技术的发展, 出现了各种发票自动识别系统, 但是发票上的印章严重影响了识别准确率. 本文提出了一种用于自动消除发票印章的SealGAN网络. SealGAN网络是基于生成式对抗网络CycleGAN的改进, 采用两个独立的分类器来取代原本的判别网络, 从而降低单个分类器的分类要求, 提高分类器的学习性能, 并且结合ResNet和Unet两种结构构建下采样−精炼−上采样的生成网络, 生成更加清晰的发票图像. 同时提出了基于风格评价和内容评价的综合评价指标对SealGAN网络进行性能评价. 实验结果表明, 与CycleGAN-ResNet和CycleGAN-Unet网络相比较, Seal GAN网络不仅能实现自动消除印章, 而且还能更加清晰地保留印章下的发票内容, 网络性能评价指标较高.
  • 图  1  生成式对抗网络结构

    Fig.  1  Generative adversarial networks structure

    图  2  CycleGAN的网络结构

    Fig.  2  GycleGAN networks structure

    图  3  SealGAN网络结构

    Fig.  3  SealGAN networks structure

    图  4  残差网络结构

    Fig.  4  Residual networks structure

    图  5  UNet网络结构示意图

    Fig.  5  Schematic diagram of UNet structure

    图  6  SealGAN生成网络结构示意图

    Fig.  6  Schematic diagram of SealGAN generative networks structure

    图  7  三种网络在不同数据集划分比例下的性能指标

    Fig.  7  Performance indices of three networks under different division proportion of data set

    图  8  基于二次分割、CycleGAN-ResNet、CycleGAN-UNet和SealGAN的印章消除对比

    Fig.  8  Comparsion of effect of the seal elimination based on re-segmentation, CycleGAN-ResNet, CycleGAN-UNet and SealGAN

    表  1  生成网络和分类器参数表

    Table  1  Parameters of the generative network and classifier

    生成网络分类器
    下采样精炼上采样
    7×7 conv, 96Residual_block(3×3, 384) ×74×4 deconv, 256, ×24×4 conv, 64, /2
    4×4 conv, 192, /24×4 deconv, 256, ×24×4 conv, 128, /2
    4×4 conv, 384, /24×4 deconv, 256, ×24×4 conv, 256, /2
    4×4 conv, 384, /24×4 deconv, 256, ×24×4 conv, 512, /2
    4×4 conv, 384, /24×4 deconv, 128, ×24×4 conv, 1
    4×4 conv, 384, /24×4 deconv, 64, ×2
    4×4 conv, 384, /27×7 conv, 3
    下载: 导出CSV

    表  2  三种网络性能评价指标

    Table  2  Performance evaluation indices of three kinds of network

    网络类型CS1CS2$ ES $
    二次分割0.3400.9911.331
    CycleGAN-ResNet0.6780.6991.377
    CycleGAN-UNet0.7030.6791.382
    SealGAN0.6990.7401.439
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-18
  • 录用日期:  2020-03-12
  • 网络出版日期:  2021-09-22
  • 刊出日期:  2021-11-18

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