Chinese-Character-and-Calligraphic-Image-Processing

Some interesting method like style transfer, GAN, deep neural networks for Chinese character and calligraphic image processing

1. Classification for 30 different Fonts

Dataset: https://pan.baidu.com/s/1LVcfD_M-pI3Vkscsb6hlow Extract code: lqp2

Part of the dataset

Fonts classification by GoogLeNet

Loss	Test accuracy	Confusion matrix

Feature visualizing

2. Style transfer for calligraphic image

Content image dataset: http://www.image-net.org/challenges/LSVRC/2012/nnoupb/ILSVRC2012_img_val.tar

Style fusion

zi2zi

The method of this application, we just simply use pix2pix to generate another style of Chinese character.

Dataset: https://pan.baidu.com/s/1JagVbA8p-Bn5OnoOErJAyQ extract code: 2vku

3. Calligraphic image denoising

4. Chinese character inpainting

Acknowledgement

These great calligraphy works are written by my teacher Prof. Zhang.

Author

1. Mingtao Guo 2. Xinran Wen

Reference

[1]. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.

[2]. Dumoulin V, Shlens J, Kudlur M. A learned representation for artistic style[J]. Proc. of ICLR, 2017, 2.

[3]. Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1125-1134.

[4]. Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[C]//European conference on computer vision. Springer, Cham, 2016: 694-711.

Code reference

[1]. Style transfer for calligraphic image: https://github.com/MingtaoGuo/Conditional-Instance-Norm-for-n-Style-Transfer

[2]. zi2zi: https://github.com/MingtaoGuo/DCGAN_WGAN_WGAN-GP_LSGAN_SNGAN_RSGAN_BEGAN_ACGAN_PGGAN_TensorFlow

[3]. Calligraphic image denoising: https://github.com/MingtaoGuo/Calligraphic-Images-Denoising-by-GAN