An Automatic System for Generating Artificial Fake Character Images

Abstract

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.