Generative adversarial classifier for handwriting characters super-resolution

Generative Adversarial Networks (GAN) receive great attention recently due to its excellent performance in image generation, transformation, and super-resolution. However, less emphasis or study has been put on GAN for classification with super-resolution. Moreover, though GANs may fabricate images which perceptually looks realistic, they usually fabricate some fake details especially in character data; this would impose further difficulties when they are input for classification. In this paper, we propose a novel Generative Adversarial Classifier (GAC) for low-resolution handwriting character recognition. Specifically, we design an additional classifier component in GAC, leading to a novel three-player GAN model which is not only able to generate high-quality super-resolved images, but also favorable for classification. Experimental results show that our proposed method can obtain remarkable performance in handwriting characters with 8 × super-resolution, achieving new state-of-the-art on benchmark dataset CASIA-HWDB1.1, and MNIST.

Duke Scholars

Author Kaizhu Huang DKU Faculty