  There is a need for information retrieval from large collections of
low-resolution (LR) binary document images, which can be found in digital
libraries across the world, where the high-resolution (HR) counterpart is not
available. This gives rise to the problem of binary document image
super-resolution (BDISR). The objective of this paper is to address the
interesting and challenging problem of super resolution of binary Tamil
document images for improved readability and better optical character
recognition (OCR). We propose multiple deep neural network architectures to
address this problem and analyze their performance. The proposed models are all
single image super-resolution techniques, which learn a generalized spatial
correspondence between the LR and HR binary document images. We employ
convolutional layers for feature extraction followed by transposed convolution
and sub-pixel convolution layers for upscaling the features. Since the outputs
of the neural networks are gray scale, we utilize the advantage of power law
transformation as a post-processing technique to improve the character level
pixel connectivity. The performance of our models is evaluated by comparing the
OCR accuracies and the mean opinion scores given by human evaluators on LR
images and the corresponding model-generated HR images.
