Next, as shown in block 206, the process flow includes generating, based on at least the one or more image super resolution algorithms, one or more high-resolution images associated with each of the one or more documents. In some embodiments, the image super resolution algorithms may be implemented using convolutional neural networks (CNN) due to its success in image classification and other applications in the computer vision fields, such as object detection, face recognition, and pedestrian detection. The efficient training implementation on modern powerful graphics processing unit (GPU) and the use of Rectified Linear Unit (ReLU) which makes convergence much faster while presenting good quality are some of the advantages of using CNN to implement the image super resolution algorithms. Accordingly, recovering a high-resolution image from a low-resolution image may be formulated using CNNs. The resulting image super resolution convolutional neural network (SRCNN) is configured to recover the high-resolution image of the document from its low-resolution counterpart in three steps, namely, patch extraction and representation, non-linear mapping, and reconstruction. In the patch extraction and representation step, the SRCNN extracts overlapping patches from the low resolution document. In response to extracting the patches, the SRCNN represents each patch as a high-dimensional vector. These vectors may comprise a set of feature maps, of which the number equals to the dimensionality of the vectors. Next, in the non-linear mapping step, the SRCNN non-linearly maps each high-dimensional vector onto another high-dimensional vector. Each mapped vector is conceptually the representation of a high-resolution patch. These vectors may comprise another set of feature maps. Next, in the reconstruction step, the high-resolution patch wise representations are aggregated to generate the final high-resolution image. In this way, the system may be configured to generate the one or more high-resolution images associated with each of the one or more documents.