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.
The research of image super-resolution can be divided into two categories: one is based on single image super-resolution (SISR), and the other is based on multiple image super-resolution (MISR) @cite_14 . Our work can be cast into the first category. We will focus on single image super-resolution (SISR) and will not further discuss approaches that reconstruct HR images from multiple images.
@cite_14
1811400895
Growing interest in super-resolution (SR) restoration of video sequences and the closed related problem of construction of SR still images from image sequences has led to the emergence of several competing methodologies. We review the state of the art of SR techniques using a taxonomy of existing techniques. We critique these methods and identified areas which promise performance improvements.