  Generative Adversarial Networks (GAN) receive great attentions recently due
to its excellent performance in image generation, transformation, and
super-resolution. However, GAN has rarely been studied and trained for
classification, leading that the generated images may not be appropriate for
classification. In this paper, we propose a novel Generative Adversarial
Classifier (GAC) particularly for low-resolution Handwriting Character
Recognition. Specifically, involving additionally a classifier in the training
process of normal GANs, GAC is calibrated for learning suitable structures and
restored characters images that benefits the classification. Experimental
results show that our proposed method can achieve remarkable performance in
handwriting characters 8x super-resolution, approximately 10% and 20% higher
than the present state-of-the-art methods respectively on benchmark data
CASIA-HWDB1.1 and MNIST.
