  Convolutional neural networks have recently demonstrated interesting results
for single image super-resolution. However, these networks were trained to deal
with super-resolution problem on natural images. In this paper, we adapt a deep
network, which was proposed for natural images superresolution, to single text
image super-resolution. To evaluate the network, we present our database for
single text image super-resolution. Moreover, we propose to combine Gradient
Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution
image. Quantitative and qualitative evaluations on our dataset show that adding
the GDL improves the super-resolution results.
