An Automatic System for Generating Artificial Fake Character Images

  • Yisheng Yue
  • Palaiahnakote Shivakumara
  • Yirui Wu
  • Liping Zhu
  • Tong LuEmail author
  • Umapada Pal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)


Due to the introduction of deep learning for text detection and recognition in natural scenes, and the increase in detecting fake images in crime applications, automatically generating fake character images has now received greater attentions. This paper presents a new system named Fake Character GAN (FCGAN). It has the ability to generate fake and artificial scene characters that have similar shapes and colors with the existing ones. The proposed method first extracts shapes and colors of character images. Then, it constructs the FCGAN, which consists of a series of convolution, residual and transposed convolution blocks. The extracted features are then fed to the FCGAN to generate fake characters and verify the quality of the generated characters simultaneously. The proposed system chooses characters from the benchmark ICDAR 2015 dataset for training, and further validated by conducting text detection and recognition experiments on input and generated fake images to show its effectiveness.


Fake characters Generative adversarial network Shape information Character editing 



This work was supported by the Natural Science Foundation of China under Grant 61672273, Grant 61832008 and Grant 61702160, the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021, Scientific Foundation of State Grid Corporation of China (Research on Ice-wind Disaster Feature Recognition and Prediction by Few-shot Machine Learning in Transmission Lines), National Key R&D Program of China under Grant 2018YFC0407901, the Fundamental Research Funds for the Central Universities under Grant 2016B14114, the Science Foundation of JiangSu under Grant BK20170892, and the open Project of the National Key Lab for Novel Software Technology in NJU under Grant K-FKT2017B05.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yisheng Yue
    • 1
  • Palaiahnakote Shivakumara
    • 2
  • Yirui Wu
    • 1
    • 3
  • Liping Zhu
    • 4
  • Tong Lu
    • 1
    Email author
  • Umapada Pal
    • 5
  1. 1.National Key Lab for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.College of Computer and InformationHohai UniversityNanjingChina
  4. 4.School of Information ManagementNanjing UniversityNanjingChina
  5. 5.Computer Vision and Pattern Recognition UnitIndian Statistical InstituteKolkataIndia

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